In this paper, the multi-objective genetic algorithm (NSGA-II) and particle swarm optimization algorithm (PSO) are combined to construct an efficient hybrid heuristic algorithm. Firstly, the algorithm uses the global exploration ability and non-dominated mechanism of NSGA-II to search widely in a broad solution space and generate a group of non-dominated solutions with uniform distribution and excellent quality. Then, the fast convergence and local optimization ability of the PSO algorithm are introduced to deeply optimize NSGA-II in order to approach the real Pareto frontier. The hybrid heuristic algorithm shows a low IGD value at the beginning of the iteration, and with the deepening of the iteration, it decreases faster and finally stabilizes at a low level. The minimum value of GD is 0.01, which also shows that the solution set distribution of the hybrid heuristic algorithm is better on the Pareto frontier. The comparison of HV values further proves that the hybrid heuristic algorithm has stronger solution set coverage ability in the target space. Combined with the case study of logistic distribution and product design, this paper verifies the effectiveness of the hybrid heuristic algorithm and can find a better trade-off solution among multiple objectives.