Abstract: Considering the fact that the optimization function of the critical slip surface search problem has many variables, complex constraints and many local extremum points, it is difficult for the traditional optimization method to achieve better search results. Therefore, a genetic algorithm based on double mutation strategy is proposed to search the critical slip surface of slope. On one hand, the algorithm improves the local optimization ability of the algorithm by detecting mutation operation and the global optimization ability of the algorithm by direct mutation operation. The combination of detection mutation operation and direct mutation operation enables the algorithm to achieve a good balance between the breadth and depth of the search. On the other hand, the algorithm adopts adaptive crossover probability and adaptive mutation probability considering individual fitness value and evolution times, so that the algorithm can increase the diversity of population in the early stage of evolution. The algorithm can protect the better individuals from destruction in the later stage of evolution. The algorithm is combined with the simplified Bishop method to calculate the examination questions provided by ACADS and a seawall slope problem. The results show that (1) for both homogeneous and heterogeneous slopes, this method can accurately search the critical slip surface of the slope and calculate the corresponding safety factor. (2) Compared with genetic algorithms that only carry out direct mutation or detect mutation, the double mutation genetic algorithm has stronger global search ability and better robustness, and has a broad application prospect.