Aiming at the problem that the gray wolf optimization algorithm is easy to fall into local optimum and low efficiency in the path planning of mobile robots, a genetic simulated annealing gray wolf optimization algorithm was proposed. An adjustable nonlinear convergence factor is used for the early search and the late search of the balance algorithm. At the same time, the adaptive genetic hybridization strategy was used to hybridize the gray wolf population with a certain probability to produce new individuals, so as to effectively enhance the diversity of the gray wolf population. The candidate wolf is accepted by simulated annealing operation at the later stage of iteration to avoid the algorithm falling into local optimal solution. The path length and path smoothness are taken as the fitness evaluation indexes and the evaluation function is established to evaluate the effect of path planning. Finally, the experimental results of path planning show that the fitness of the improved algorithm in this paper is optimized by 2.10, 3.15 and 3.94 respectively compared with the gray wolf optimization algorithm on three maps of different sizes, and the path planning effect is significantly better than other related algorithms.