Abstract: The grid strategy conducts simple and fast analysis of the diversity of the multi-particle swarm optimization algorithm. However, because of limited resolution, this strategy is unable to determine the diversity of a hypercube with the same number of particles. Thus, we propose a dynamic grid strategy. Through dynamic partitioning of the target space and dynamic adjustment of the hypercube, the same density of the hypercube is sorted. This approach is better for archive management and selection of leading particles. We exchange genes in the archival solution, which was previously neglected, to improve the convergence rate of the algorithm. Verification of DTLZ function proved that the algorithm has good diversity and faster convergence speed in high-dimension multi-objective optimization, thereby effectively solving such problem.