In order to solve the problems of small search scale, slow convergence speed and imbalance between global search and local search in current path optimization algorithms, a multi-strategy fusion of grey wolf optimization algorithm (MGWO) is proposed. The quality of the initial solution is improved by introducing the elite reverse optimization strategy to initialize the population. An adaptive weight mechanism is used to dynamically adjust the leadership of the optimal wolf. The ability of balancing local search and global exploration is improved through the piecewise search method. The simulation results show that the algorithm performs well, can quickly find the optimal path, and improve the overall performance of the algorithm, which has a certain reference.