In complex environments, traditional path planning algorithms are prone to issues such as local optima. To address this, we propose a Multi-Strategy Harris Hawks Optimization (MSHHO) algorithm. First, the Harris Hawks population is initialized using chaotic mapping to enhance diversity. Second, crisscross optimization strategy is introduced to improve the algorithm’s global search capability, accelerate convergence, and escape local optima. Finally, a convergence factor update strategy is proposed to dynamically adjust the prey’s escape energy, balancing global exploration and local exploitation. To verify the algorithm’s effectiveness, the proposed MSHHO is comprehensively tested against five other metaheuristic optimization algorithms in dual-grid map pathfinding experiments. Experimental results demonstrate that MSHHO achieves superior performance and shorter optimal paths compared to competing methods.