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In order to solve the problems of low search efficiency and easily falling into the local optimal solution in traditional genetic algorithm, an improved genetic algorithm is proposed in this paper. It adopts the simple one-dimensional code to replace the complex two-dimensional coding, which can save storage space. In the design of genetic operators, many operations such as crossover and mutation are redefined to avoid getting into the local optimum. Then the two fitness functions-collision-free path and the shortest distance- are fused into one for the following genetic optimization. In the case of the same population parameters, 100 trials are respectively developed with the method of improved genetic algorithm and traditional genetic algorithm. Among them, the improved genetic algorithm to search the optimal path gets to 95 times, and the shortest path is 20.970 6. Besides, the average searching time takes up 217 ms. While the number of traditional method to search for the optimal path reaches up to 62 times, the shortest path can be 25.071 1, and the average searching time needs 345 ms. So compared to the tests results referred above, the improved genetic algorithm is more efficient and can get a better solution than the traditional genetic algorithm.