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In both industrial and agricultural sectors, robots frequently encounter complex scenarios that consist of numerous discontinuous and discrete local paths, forming challenging trajectories. Rational motion planning serves as the primary foundation for robots to achieve their expected operational goals. A multi-objective comprehensive optimization method was proposed based on the non-dominated sorting genetic algorithmⅡ (NSGA-Ⅱ). The algorithm operated on the principle of hierarchical sorting based on the dominance relationship between individuals, and introduced a “crowding distance” index to characterize the diversity between individuals, thereby providing robust support for maintaining population diversity during the genetic process. Simultaneously, a kinematic model of the robot was established, and a path sequence optimization function was constructed to reduce the robot’s unloaded travel distance, motion time, and joint impact. Higher-order spline fitting and interpolation planning were implemented in Cartesian and joint spaces, significantly enhancing the smoothness and geometric characteristics of the spatial trajectory. The main contribution lied in generating a spatial Pareto optimal frontier solution set based on NSGA-Ⅱ, which effectively solved the multi-objective optimization problem under constraints such as short robot motion time, small joint impact, and optimal task path. After optimization, the robot’s travel path length was reduced by 74%, operational efficiency was improved by 33.44%, and joint stability was enhanced by an average of 50.97%. Through simulation and experimentation, the algorithm’s significant effectiveness in improving robot motion efficiency, continuity, and non-mutability was verified.