diff --git a/.flake8 b/.flake8
index ab63c931..10742c16 100644
--- a/.flake8
+++ b/.flake8
@@ -5,3 +5,4 @@ ignore =
     W503 line break before binary operator
     W293 blank line contains whitespace
     W291 trailing whitespace
+    W391 blank line at end of file
diff --git a/requirements.txt b/requirements.txt
index 0706dba5..8eaa8cf4 100644
--- a/requirements.txt
+++ b/requirements.txt
@@ -17,3 +17,4 @@ tigeropen
 rqdatac
 ta-lib
 ibapi
+deap
\ No newline at end of file
diff --git a/tests/backtesting/GA_Pre_Final.ipynb b/tests/backtesting/GA_Pre_Final.ipynb
index 8519ad3a..de3b6d46 100644
--- a/tests/backtesting/GA_Pre_Final.ipynb
+++ b/tests/backtesting/GA_Pre_Final.ipynb
@@ -10,28 +10,19 @@
     "import multiprocessing\n",
     "import numpy as np\n",
     "from deap import creator, base, tools, algorithms\n",
-    "from backtesting import BacktestingEngine,OptimizationSetting\n",
-    "from boll_channel_strategy import BollChannelStrategy\n",
-    "from atr_rsi_strategy import AtrRsiStrategy\n",
+    "from vnpy.app.cta_strategy.backtesting import BacktestingEngine,OptimizationSetting\n",
+    "from vnpy.app.cta_strategy.strategies.boll_channel_strategy import BollChannelStrategy\n",
+    "from vnpy.app.cta_strategy.strategies.atr_rsi_strategy import AtrRsiStrategy\n",
     "from datetime import datetime\n",
     "import multiprocessing           #多进程\n",
-    "from scoop import futures        #多进程\n",
     "from functools import lru_cache"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "数据总体： 13824\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "setting = OptimizationSetting()\n",
     "#setting.add_parameter('atr_length', 10, 50, 2)\n",
@@ -51,20 +42,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "dict_keys(['boll_window', 'cci_window', 'atr_window'])"
-      ]
-     },
-     "execution_count": 3,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "setting_names = random.choice(local_setting).keys()\n",
     "setting_names"
@@ -72,7 +52,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -83,40 +63,18 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[22, 28, 22]"
-      ]
-     },
-     "execution_count": 5,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "parameter_generate()"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "{'boll_window': 24, 'cci_window': 14, 'atr_window': 28}"
-      ]
-     },
-     "execution_count": 6,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "setting=dict(zip(setting_names,parameter_generate()))\n",
     "setting"
@@ -124,7 +82,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -168,27 +126,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "(-0.51, -0.28)"
-      ]
-     },
-     "execution_count": 8,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "object_func(parameter_generate())"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -203,7 +150,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -224,8 +171,8 @@
     "    toolbox.register(\"mutate\", tools.mutUniformInt,low = 4,up = 40,indpb=1)               \n",
     "    toolbox.register(\"evaluate\", object_func)                                                \n",
     "    toolbox.register(\"select\", tools.selNSGA2)       \n",
-    "    #pool = multiprocessing.Pool()\n",
-    "    #toolbox.register(\"map\", pool.map)\n",
+    "    pool = multiprocessing.Pool(multiprocessing.cpu_count())\n",
+    "    toolbox.register(\"map\", pool.map)\n",
     "    #toolbox.register(\"map\", futures.map)\n",
     "    \n",
     "    \n",
@@ -266,55 +213,11 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": null,
    "metadata": {
     "scrolled": false
    },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "开始运行遗传算法，每代族群总数：34, 优良品种筛选个数：27，迭代次数：30，交叉概率：0.95，突变概率：0.05\n",
-      "gen\tnevals\tmean                   \tstd                    \tmin          \tmax        \n",
-      "0  \t34    \t[0.08852941 0.00352941]\t[0.5373362  0.29107188]\t[-0.7  -0.63]\t[1.51 0.5 ]\n",
-      "1  \t34    \t[0.60148148 0.27518519]\t[0.31013383 0.08573734]\t[0.32 0.18]  \t[1.51 0.5 ]\n",
-      "2  \t34    \t[0.79333333 0.33851852]\t[0.27758215 0.06742369]\t[0.47 0.25]  \t[1.54 0.5 ]\n",
-      "3  \t34    \t[1.00888889 0.39777778]\t[0.3147525  0.06214281]\t[0.7  0.33]  \t[1.54 0.5 ]\n",
-      "4  \t34    \t[1.41074074 0.47444444]\t[0.22881217 0.04661373]\t[0.96 0.36]  \t[1.92 0.57]\n",
-      "5  \t34    \t[1.59666667 0.51222222]\t[0.14714568 0.0255797 ]\t[1.51 0.49]  \t[1.92 0.57]\n",
-      "6  \t34    \t[1.66259259 0.52185185]\t[0.16585564 0.02981884]\t[1.52 0.49]  \t[1.92 0.57]\n",
-      "7  \t34    \t[1.8737037  0.55666667]\t[0.07713135 0.01763834]\t[1.75 0.53]  \t[1.95 0.57]\n",
-      "8  \t34    \t[1.93666667 0.57      ]\t[0.01490712 0.        ]\t[1.92 0.57]  \t[1.95 0.57]\n",
-      "9  \t34    \t[1.95 0.57]            \t[0. 0.]                \t[1.95 0.57]  \t[1.95 0.57]\n",
-      "10 \t34    \t[1.95 0.57]            \t[0. 0.]                \t[1.95 0.57]  \t[1.95 0.57]\n",
-      "11 \t34    \t[1.95 0.57]            \t[0. 0.]                \t[1.95 0.57]  \t[1.95 0.57]\n",
-      "12 \t34    \t[1.95 0.57]            \t[0. 0.]                \t[1.95 0.57]  \t[1.95 0.57]\n",
-      "13 \t34    \t[1.95 0.57]            \t[0. 0.]                \t[1.95 0.57]  \t[1.95 0.57]\n",
-      "14 \t34    \t[1.95 0.57]            \t[0. 0.]                \t[1.95 0.57]  \t[1.95 0.57]\n",
-      "15 \t34    \t[1.95 0.57]            \t[0. 0.]                \t[1.95 0.57]  \t[1.95 0.57]\n",
-      "16 \t34    \t[1.95 0.57]            \t[0. 0.]                \t[1.95 0.57]  \t[1.95 0.57]\n",
-      "17 \t34    \t[1.95 0.57]            \t[0. 0.]                \t[1.95 0.57]  \t[1.95 0.57]\n",
-      "18 \t34    \t[1.95 0.57]            \t[0. 0.]                \t[1.95 0.57]  \t[1.95 0.57]\n",
-      "19 \t34    \t[1.95 0.57]            \t[0. 0.]                \t[1.95 0.57]  \t[1.95 0.57]\n",
-      "20 \t34    \t[1.95 0.57]            \t[0. 0.]                \t[1.95 0.57]  \t[1.95 0.57]\n",
-      "21 \t34    \t[1.95 0.57]            \t[0. 0.]                \t[1.95 0.57]  \t[1.95 0.57]\n",
-      "22 \t34    \t[1.95 0.57]            \t[0. 0.]                \t[1.95 0.57]  \t[1.95 0.57]\n",
-      "23 \t34    \t[1.95 0.57]            \t[0. 0.]                \t[1.95 0.57]  \t[1.95 0.57]\n",
-      "24 \t34    \t[1.95 0.57]            \t[0. 0.]                \t[1.95 0.57]  \t[1.95 0.57]\n",
-      "25 \t34    \t[1.95 0.57]            \t[0. 0.]                \t[1.95 0.57]  \t[1.95 0.57]\n",
-      "26 \t34    \t[1.95 0.57]            \t[0. 0.]                \t[1.95 0.57]  \t[1.95 0.57]\n",
-      "27 \t34    \t[1.95 0.57]            \t[0. 0.]                \t[1.95 0.57]  \t[1.95 0.57]\n",
-      "28 \t34    \t[1.95 0.57]            \t[0. 0.]                \t[1.95 0.57]  \t[1.95 0.57]\n",
-      "29 \t34    \t[1.95 0.57]            \t[0. 0.]                \t[1.95 0.57]  \t[1.95 0.57]\n",
-      "30 \t34    \t[1.95 0.57]            \t[0. 0.]                \t[1.95 0.57]  \t[1.95 0.57]\n",
-      "遗传算法优化完成，耗时309秒\n",
-      "输出帕累托前沿解集：\n",
-      "{'boll_window': 48, 'cci_window': 40, 'atr_window': 22, 'return_drawdown_ratio': 1.95, 'sharpe_ratio': 0.57}\n",
-      "{'boll_window': 48, 'cci_window': 50, 'atr_window': 22, 'return_drawdown_ratio': 1.95, 'sharpe_ratio': 0.57}\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "optimize()"
    ]
diff --git a/tests/backtesting/run_ga.py b/tests/backtesting/run_ga.py
new file mode 100644
index 00000000..4ce1c6b3
--- /dev/null
+++ b/tests/backtesting/run_ga.py
@@ -0,0 +1,27 @@
+from vnpy.app.cta_strategy.backtesting import BacktestingEngine, OptimizationSetting
+from vnpy.app.cta_strategy.strategies.atr_rsi_strategy import (
+    AtrRsiStrategy,
+)
+from datetime import datetime
+
+if __name__ == "__main__":
+    engine = BacktestingEngine()
+    engine.set_parameters(
+        vt_symbol="IF88.CFFEX",
+        interval="1m",
+        start=datetime(2019, 1, 1),
+        end=datetime(2019, 4, 30),
+        rate=0.3 / 10000,
+        slippage=0.2,
+        size=300,
+        pricetick=0.2,
+        capital=1_000_000,
+    )
+    engine.add_strategy(AtrRsiStrategy, {})
+
+    setting = OptimizationSetting()
+    setting.set_target("sharpe_ratio")
+    setting.add_parameter("atr_length", 3, 39, 1)
+    setting.add_parameter("atr_ma_length", 10, 30, 1)
+
+    engine.run_ga_optimization(setting)
diff --git a/tests/backtesting/turtle.ipynb b/tests/backtesting/turtle.ipynb
index 9807424b..a3a4db10 100644
--- a/tests/backtesting/turtle.ipynb
+++ b/tests/backtesting/turtle.ipynb
@@ -16,7 +16,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 2,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -54,115 +54,87 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
-   "metadata": {},
+   "execution_count": 3,
+   "metadata": {
+    "scrolled": true
+   },
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "2019-04-15 22:19:49.696835\t参数：{'atr_length': 22}, 目标：121.19996051999999\n",
-      "2019-04-15 22:19:49.709531\t参数：{'atr_length': 23}, 目标：116.54901966000013\n",
-      "2019-04-15 22:19:49.710507\t参数：{'atr_length': 24}, 目标：113.29820520000014\n"
+      "2019-05-03 16:19:04.193703\t开始运行遗传算法，每代族群总数：11, 优良品种筛选个数：8，迭代次数：30，交叉概率：0.95，突变概率：0.050000000000000044\n",
+      "gen\tnevals\tmean        \tstd         \tmin         \tmax        \n",
+      "0  \t11    \t[0.58423524]\t[0.30377007]\t[0.13231977]\t[1.2382818]\n",
+      "1  \t11    \t[0.90248989]\t[0.15747112]\t[0.68707859]\t[1.2382818]\n",
+      "2  \t11    \t[1.09406088]\t[0.18860523]\t[0.86284921]\t[1.46762684]\n",
+      "3  \t11    \t[1.21413386]\t[0.12138014]\t[1.02072108]\t[1.46762684]\n",
+      "4  \t11    \t[1.29561806]\t[0.09930932]\t[1.2382818] \t[1.46762684]\n",
+      "5  \t11    \t[1.41029058]\t[0.09930932]\t[1.2382818] \t[1.46762684]\n",
+      "6  \t11    \t[1.46762684]\t[0.]        \t[1.46762684]\t[1.46762684]\n",
+      "7  \t11    \t[1.46762684]\t[0.]        \t[1.46762684]\t[1.46762684]\n",
+      "8  \t11    \t[1.46762684]\t[0.]        \t[1.46762684]\t[1.46762684]\n",
+      "9  \t11    \t[1.46762684]\t[0.]        \t[1.46762684]\t[1.46762684]\n",
+      "10 \t11    \t[1.46762684]\t[0.]        \t[1.46762684]\t[1.46762684]\n",
+      "11 \t11    \t[1.46762684]\t[0.]        \t[1.46762684]\t[1.46762684]\n",
+      "12 \t11    \t[1.46762684]\t[0.]        \t[1.46762684]\t[1.46762684]\n",
+      "13 \t11    \t[1.46762684]\t[0.]        \t[1.46762684]\t[1.46762684]\n",
+      "14 \t11    \t[1.46762684]\t[0.]        \t[1.46762684]\t[1.46762684]\n",
+      "15 \t11    \t[1.46762684]\t[0.]        \t[1.46762684]\t[1.46762684]\n",
+      "16 \t11    \t[1.46762684]\t[0.]        \t[1.46762684]\t[1.46762684]\n",
+      "17 \t11    \t[1.46762684]\t[0.]        \t[1.46762684]\t[1.46762684]\n",
+      "18 \t11    \t[1.46762684]\t[0.]        \t[1.46762684]\t[1.46762684]\n",
+      "19 \t11    \t[1.46762684]\t[0.]        \t[1.46762684]\t[1.46762684]\n",
+      "20 \t11    \t[1.46762684]\t[0.]        \t[1.46762684]\t[1.46762684]\n",
+      "21 \t11    \t[1.46762684]\t[0.]        \t[1.46762684]\t[1.46762684]\n",
+      "22 \t11    \t[1.46762684]\t[0.]        \t[1.46762684]\t[1.46762684]\n",
+      "23 \t11    \t[1.46762684]\t[0.]        \t[1.46762684]\t[1.46762684]\n",
+      "24 \t11    \t[1.46762684]\t[0.]        \t[1.46762684]\t[1.46762684]\n",
+      "25 \t11    \t[1.46762684]\t[0.]        \t[1.46762684]\t[1.46762684]\n",
+      "26 \t11    \t[1.46762684]\t[0.]        \t[1.46762684]\t[1.46762684]\n",
+      "27 \t11    \t[1.46762684]\t[0.]        \t[1.46762684]\t[1.46762684]\n",
+      "28 \t11    \t[1.46762684]\t[0.]        \t[1.46762684]\t[1.46762684]\n",
+      "29 \t11    \t[1.46762684]\t[0.]        \t[1.46762684]\t[1.46762684]\n",
+      "30 \t11    \t[1.46762684]\t[0.]        \t[1.46762684]\t[1.46762684]\n",
+      "2019-05-03 16:19:58.256354\t遗传算法优化完成，耗时54秒\n"
      ]
     },
     {
      "data": {
       "text/plain": [
-       "[(\"{'atr_length': 22}\",\n",
-       "  121.19996051999999,\n",
-       "  {'start_date': datetime.date(2013, 1, 18),\n",
-       "   'end_date': datetime.date(2019, 4, 11),\n",
-       "   'total_days': 1514,\n",
-       "   'profit_days': 763,\n",
-       "   'loss_days': 750,\n",
-       "   'capital': 1000000,\n",
-       "   'end_balance': 2211999.6052,\n",
-       "   'max_drawdown': -248787.6971999996,\n",
-       "   'max_ddpercent': -12.636908338002794,\n",
-       "   'total_net_pnl': 1211999.6052000003,\n",
-       "   'daily_net_pnl': 800.5281408190227,\n",
-       "   'total_commission': 242400.39479999998,\n",
-       "   'daily_commission': 160.10594108322323,\n",
-       "   'total_slippage': 481860.0,\n",
-       "   'daily_slippage': 318.2694848084544,\n",
-       "   'total_turnover': 8080013160.0,\n",
-       "   'daily_turnover': 5336864.702774108,\n",
-       "   'total_trade_count': 8031,\n",
-       "   'daily_trade_count': 5.30449141347424,\n",
-       "   'total_return': 121.19996051999999,\n",
-       "   'annual_return': 19.212675379656538,\n",
-       "   'daily_return': 0.052348808029058974,\n",
-       "   'return_std': 0.9487639654919149,\n",
-       "   'sharpe_ratio': 0.854779772691872,\n",
-       "   'return_drawdown_ratio': 9.590950355754112}),\n",
-       " (\"{'atr_length': 23}\",\n",
-       "  116.54901966000013,\n",
-       "  {'start_date': datetime.date(2013, 1, 18),\n",
-       "   'end_date': datetime.date(2019, 4, 11),\n",
-       "   'total_days': 1514,\n",
-       "   'profit_days': 759,\n",
-       "   'loss_days': 754,\n",
-       "   'capital': 1000000,\n",
-       "   'end_balance': 2165490.1966000013,\n",
-       "   'max_drawdown': -232904.1239999996,\n",
-       "   'max_ddpercent': -13.536251422505968,\n",
-       "   'total_net_pnl': 1165490.1966000004,\n",
-       "   'daily_net_pnl': 769.8085842800531,\n",
-       "   'total_commission': 242769.80339999998,\n",
-       "   'daily_commission': 160.34993619550858,\n",
-       "   'total_slippage': 482700.0,\n",
-       "   'daily_slippage': 318.82430647291943,\n",
-       "   'total_turnover': 8092326780.0,\n",
-       "   'daily_turnover': 5344997.873183619,\n",
-       "   'total_trade_count': 8045,\n",
-       "   'daily_trade_count': 5.313738441215324,\n",
-       "   'total_return': 116.54901966000013,\n",
-       "   'annual_return': 18.475406022721288,\n",
-       "   'daily_return': 0.0509452313711608,\n",
-       "   'return_std': 0.961380153488665,\n",
-       "   'sharpe_ratio': 0.8209448965768181,\n",
-       "   'return_drawdown_ratio': 8.610139987960078}),\n",
-       " (\"{'atr_length': 24}\",\n",
-       "  113.29820520000014,\n",
-       "  {'start_date': datetime.date(2013, 1, 18),\n",
-       "   'end_date': datetime.date(2019, 4, 11),\n",
-       "   'total_days': 1514,\n",
-       "   'profit_days': 760,\n",
-       "   'loss_days': 753,\n",
-       "   'capital': 1000000,\n",
-       "   'end_balance': 2132982.0520000015,\n",
-       "   'max_drawdown': -236503.9475999996,\n",
-       "   'max_ddpercent': -13.23872340727957,\n",
-       "   'total_net_pnl': 1132982.0520000013,\n",
-       "   'daily_net_pnl': 748.3368903566719,\n",
-       "   'total_commission': 242817.948,\n",
-       "   'daily_commission': 160.3817357992074,\n",
-       "   'total_slippage': 482700.0,\n",
-       "   'daily_slippage': 318.82430647291943,\n",
-       "   'total_turnover': 8093931600.0,\n",
-       "   'daily_turnover': 5346057.85997358,\n",
-       "   'total_trade_count': 8045,\n",
-       "   'daily_trade_count': 5.313738441215324,\n",
-       "   'total_return': 113.29820520000014,\n",
-       "   'annual_return': 17.96008536856013,\n",
-       "   'daily_return': 0.049946173936258026,\n",
-       "   'return_std': 0.959328411709829,\n",
-       "   'sharpe_ratio': 0.8065671672003681,\n",
-       "   'return_drawdown_ratio': 8.558091419728651})]"
+       "[({'atr_length': 38, 'atr_ma_length': 25}, 1.4676268402266743)]"
       ]
      },
-     "execution_count": 5,
+     "execution_count": 3,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
     "setting = OptimizationSetting()\n",
-    "setting.set_target(\"total_return\")\n",
-    "setting.add_parameter(\"atr_length\", 22, 24, 1)\n",
+    "setting.set_target(\"sharpe_ratio\")\n",
+    "setting.add_parameter(\"atr_length\", 3, 39, 1)\n",
+    "setting.add_parameter(\"atr_ma_length\", 10, 30, 1)\n",
     "\n",
-    "engine.run_optimization(setting)"
+    "engine.run_ga_optimization(setting)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "result = _"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "print(result)"
    ]
   },
   {
diff --git a/vnpy/app/cta_backtester/engine.py b/vnpy/app/cta_backtester/engine.py
index 9da8e6f7..573feefe 100644
--- a/vnpy/app/cta_backtester/engine.py
+++ b/vnpy/app/cta_backtester/engine.py
@@ -222,20 +222,25 @@ class BacktesterEngine(BaseEngine):
         return strategy_class.get_class_parameters()
 
     def run_optimization(
-            self,
-            class_name: str,
-            vt_symbol: str,
-            interval: str,
-            start: datetime,
-            end: datetime,
-            rate: float,
-            slippage: float,
-            size: int,
-            pricetick: float,
-            capital: int,
-            optimization_setting: OptimizationSetting):
+        self,
+        class_name: str,
+        vt_symbol: str,
+        interval: str,
+        start: datetime,
+        end: datetime,
+        rate: float,
+        slippage: float,
+        size: int,
+        pricetick: float,
+        capital: int,
+        optimization_setting: OptimizationSetting,
+        use_ga: bool
+    ):
         """"""
-        self.write_log("开始多进程参数优化")
+        if use_ga:
+            self.write_log("开始遗传算法参数优化")
+        else:
+            self.write_log("开始多进程参数优化")
 
         self.result_values = None
 
@@ -260,10 +265,16 @@ class BacktesterEngine(BaseEngine):
             {}
         )
 
-        self.result_values = engine.run_optimization(
-            optimization_setting,
-            output=False
-        )
+        if use_ga:
+            self.result_values = engine.run_ga_optimization(
+                optimization_setting,
+                output=False
+            )
+        else:
+            self.result_values = engine.run_optimization(
+                optimization_setting,
+                output=False
+            )
 
         # Clear thread object handler.
         self.thread = None
@@ -285,7 +296,8 @@ class BacktesterEngine(BaseEngine):
         size: int,
         pricetick: float,
         capital: int,
-        optimization_setting: OptimizationSetting
+        optimization_setting: OptimizationSetting,
+        use_ga: bool
     ):
         if self.thread:
             self.write_log("已有任务在运行中，请等待完成")
@@ -305,7 +317,8 @@ class BacktesterEngine(BaseEngine):
                 size,
                 pricetick,
                 capital,
-                optimization_setting
+                optimization_setting,
+                use_ga
             )
         )
         self.thread.start()
diff --git a/vnpy/app/cta_backtester/ui/widget.py b/vnpy/app/cta_backtester/ui/widget.py
index 74eddac9..2198c11d 100644
--- a/vnpy/app/cta_backtester/ui/widget.py
+++ b/vnpy/app/cta_backtester/ui/widget.py
@@ -240,7 +240,7 @@ class BacktesterManager(QtWidgets.QWidget):
         if i != dialog.Accepted:
             return
 
-        optimization_setting = dialog.get_setting()
+        optimization_setting, use_ga = dialog.get_setting()
         self.target_display = dialog.target_display
 
         self.backtester_engine.start_optimization(
@@ -254,7 +254,8 @@ class BacktesterManager(QtWidgets.QWidget):
             size,
             pricetick,
             capital,
-            optimization_setting
+            optimization_setting,
+            use_ga
         )
 
         self.result_button.setEnabled(False)
@@ -592,6 +593,7 @@ class OptimizationSettingEditor(QtWidgets.QDialog):
         self.edits = {}
 
         self.optimization_setting = None
+        self.use_ga = False
 
         self.init_ui()
 
@@ -642,12 +644,27 @@ class OptimizationSettingEditor(QtWidgets.QDialog):
 
             row += 1
 
-        button = QtWidgets.QPushButton("确定")
-        button.clicked.connect(self.generate_setting)
-        grid.addWidget(button, row, 0, 1, 4)
+        parallel_button = QtWidgets.QPushButton("多进程优化")
+        parallel_button.clicked.connect(self.generate_parallel_setting)
+        grid.addWidget(parallel_button, row, 0, 1, 4)
+
+        row += 1
+        ga_button = QtWidgets.QPushButton("遗传算法优化")
+        ga_button.clicked.connect(self.generate_ga_setting)
+        grid.addWidget(ga_button, row, 0, 1, 4)
 
         self.setLayout(grid)
 
+    def generate_ga_setting(self):
+        """"""
+        self.use_ga = True
+        self.generate_setting()
+
+    def generate_parallel_setting(self):
+        """"""
+        self.use_ga = False
+        self.generate_setting()
+
     def generate_setting(self):
         """"""
         self.optimization_setting = OptimizationSetting()
@@ -676,7 +693,7 @@ class OptimizationSettingEditor(QtWidgets.QDialog):
 
     def get_setting(self):
         """"""
-        return self.optimization_setting
+        return self.optimization_setting, self.use_ga
 
 
 class OptimizationResultMonitor(QtWidgets.QDialog):
diff --git a/vnpy/app/cta_strategy/backtesting.py b/vnpy/app/cta_strategy/backtesting.py
index e4aa25c7..fc4875ce 100644
--- a/vnpy/app/cta_strategy/backtesting.py
+++ b/vnpy/app/cta_strategy/backtesting.py
@@ -3,12 +3,16 @@ from datetime import date, datetime
 from typing import Callable
 from itertools import product
 from functools import lru_cache
+from time import time
 import multiprocessing
+import random
+import math
 
 import numpy as np
 import matplotlib.pyplot as plt
 import seaborn as sns
 from pandas import DataFrame
+from deap import creator, base, tools, algorithms
 
 from vnpy.trader.constant import (Direction, Offset, Exchange, 
                                   Interval, Status)
@@ -26,6 +30,8 @@ from .base import (
 from .template import CtaTemplate
 
 sns.set_style("whitegrid")
+creator.create("FitnessMax", base.Fitness, weights=(1.0,))
+creator.create("Individual", list, fitness=creator.FitnessMax)
 
 
 class OptimizationSetting:
@@ -514,6 +520,124 @@ class BacktestingEngine:
 
         return result_values
 
+    def run_ga_optimization(self, optimization_setting: OptimizationSetting, output=True):
+        """"""
+        # Get optimization setting and target
+        settings = optimization_setting.generate_setting()
+        target_name = optimization_setting.target_name
+
+        if not settings:
+            self.output("优化参数组合为空，请检查")
+            return
+
+        if not target_name:
+            self.output("优化目标未设置，请检查")
+            return
+
+        # Define parameter generation function
+        def generate_parameter():
+            """"""
+            return list(random.choice(settings).values())
+
+        # Create ga object function
+        global ga_target_name
+        global ga_strategy_class
+        global ga_setting
+        global ga_vt_symbol
+        global ga_interval
+        global ga_start
+        global ga_rate
+        global ga_slippage
+        global ga_size
+        global ga_pricetick
+        global ga_capital
+        global ga_end
+        global ga_mode
+
+        ga_target_name = target_name
+        ga_strategy_class = self.strategy_class
+        ga_setting = settings[0]
+        ga_vt_symbol = self.vt_symbol
+        ga_interval = self.interval
+        ga_start = self.start
+        ga_rate = self.rate
+        ga_slippage = self.slippage
+        ga_size = self.size
+        ga_pricetick = self.pricetick
+        ga_capital = self.capital
+        ga_end = self.end
+        ga_mode = self.mode
+
+        # Set up genetic algorithem
+        toolbox = base.Toolbox() 
+        toolbox.register("individual", tools.initIterate, creator.Individual, generate_parameter)                          
+        toolbox.register("population", tools.initRepeat, list, toolbox.individual)                                            
+        toolbox.register("mate", tools.cxTwoPoint)                                               
+        toolbox.register("mutate", tools.mutUniformInt, low=4, up=40, indpb=1)               
+        toolbox.register("evaluate", ga_optimize)                                                
+        toolbox.register("select", tools.selNSGA2)       
+
+        total_size = len(settings)
+        pop_size = int(pow(total_size, 1 / math.e))     # number of individuals in each generation
+        lambda_ = pop_size                              # number of children to produce at each generation
+        mu = int(pop_size * 0.8)                        # number of individuals to select for the next generation
+
+        cxpb = 0.95         # probability that an offspring is produced by crossover    
+        mutpb = 1 - cxpb    # probability that an offspring is produced by mutation
+        ngen = 30           # number of generation
+                
+        pop = toolbox.population(pop_size)      
+        hof = tools.ParetoFront()               # end result of pareto front
+
+        stats = tools.Statistics(lambda ind: ind.fitness.values)
+        np.set_printoptions(suppress=True)
+        stats.register("mean", np.mean, axis=0)
+        stats.register("std", np.std, axis=0)
+        stats.register("min", np.min, axis=0)
+        stats.register("max", np.max, axis=0)
+
+        # Multiprocessing is not supported yet.
+        # pool = multiprocessing.Pool(multiprocessing.cpu_count())
+        # toolbox.register("map", pool.map)
+
+        # Run ga optimization
+        self.output(f"参数优化空间：{total_size}")
+        self.output(f"每代族群总数：{pop_size}")
+        self.output(f"优良筛选个数：{mu}")
+        self.output(f"迭代次数：{ngen}")
+        self.output(f"交叉概率：{cxpb:.0%}")
+        self.output(f"突变概率：{mutpb:.0%}")
+
+        start = time()
+
+        algorithms.eaMuPlusLambda(
+            pop, 
+            toolbox, 
+            mu, 
+            lambda_, 
+            cxpb, 
+            mutpb, 
+            ngen, 
+            stats,
+            halloffame=hof
+        )    
+        
+        end = time()
+        cost = int((end - start))
+
+        self.output(f"遗传算法优化完成，耗时{cost}秒")
+        
+        # Return result list
+        results = []
+        parameter_keys = list(ga_setting.keys())
+
+        for parameter_values in hof:
+            setting = dict(zip(parameter_keys, parameter_values))
+            target_value = ga_optimize(parameter_values)[0]
+            results.append((setting, target_value, {}))
+        
+        return results
+
     def update_daily_close(self, price: float):
         """"""
         d = self.datetime.date()
@@ -939,12 +1063,13 @@ def optimize(
     pricetick: float,
     capital: int,
     end: datetime,
-    mode: BacktestingMode,
+    mode: BacktestingMode
 ):
     """
     Function for running in multiprocessing.pool
     """
     engine = BacktestingEngine()
+    
     engine.set_parameters(
         vt_symbol=vt_symbol,
         interval=interval,
@@ -968,6 +1093,35 @@ def optimize(
     return (str(setting), target_value, statistics)
 
 
+@lru_cache(maxsize=1000000)
+def _ga_optimizae(parameter_values: tuple):
+    """"""
+    parameter_keys = list(ga_setting.keys())
+    setting = dict(zip(parameter_keys, parameter_values))
+
+    result = optimize(
+        ga_target_name,
+        ga_strategy_class,
+        setting,
+        ga_vt_symbol,
+        ga_interval,
+        ga_start,
+        ga_rate,
+        ga_slippage,
+        ga_size,
+        ga_pricetick,
+        ga_capital,
+        ga_end,
+        ga_mode
+    )
+    return (result[1],)
+
+
+def ga_optimize(parameter_values: list):
+    """"""
+    return _ga_optimizae(tuple(parameter_values))
+
+
 @lru_cache(maxsize=10)
 def load_bar_data(
     symbol: str,
@@ -993,3 +1147,19 @@ def load_tick_data(
     return database_manager.load_tick_data(
         symbol, exchange, start, end
     )
+
+
+# GA related global value
+ga_end = None
+ga_mode = None
+ga_target_name = None
+ga_strategy_class = None
+ga_setting = None
+ga_vt_symbol = None
+ga_interval = None
+ga_start = None
+ga_rate = None
+ga_slippage = None
+ga_size = None
+ga_pricetick = None
+ga_capital = None
\ No newline at end of file