We propose a path planning method for mobile robots on the basis of multi-strategy hybridartificial fish swarm algorithm (MH-AFSA). We introduce a multi-strategy hybrid strategy to improve the convergence speed and global search ability of the traditional artificial fish swarm algorithm (AFSA). The vision of the artificial fish is enlarged by using the weighted average distance strategy. The log function is used as the step size factor, which overcomes the limitation of the traditional fixed step size. Gauss mutation strategy is used to expand the diversity of the population. The performance of the proposed MH-AFSA is tested by classical function optimization and the traveling salesman problem (TSP). The environment model of mobile robots is established, and the path planning method based on MH-AFSA is presented. Numerical simulations demonstrate the superiority and effectiveness of the proposed method.