This study adopted Genetic Algorithm (GA) as the main path planning strategy. GA is a heuristic search and optimization technique with global search capability and adaptability suitable for multi-objective problems. This article conducts experiments on the impact of fitness function weights on algorithm performance. The research results show that as the weight of path length increases, the average path length decreases from 1,195 to 1,150, indicating that higher path length weights tend to generate shorter paths. When the average obstacle avoidance ability weight increases from 0.1 to 0.2, the average obstacle avoidance ability rises from 90% to 94%, indicating that a larger weight enhances the algorithm’s obstacle avoidance ability. The average time cost decreases from 325 min to 305 min with the change of time cost weight, reflecting the impact of weight adjustment on the algorithm running time. Explained how adjusting the fitness function with different weights affects the algorithm’s performance in terms of path length, obstacle avoidance ability, and time cost, which helps to select appropriate weight combinations based on the needs of specific application scenarios.