In multimodal multiobjective optimization problems, the existence of multiple equivalent Pareto optimal sets poses a challenge for optimization algorithms to balance convergence and diversity. Existing algorithms still have room for performance improvement in handling such problems. Therefore, this paper proposes a novel multimodal multiobjective optimization algorithm. Its key innovation lies in the introduction of a proportional selection strategy during the environmental selection process. By rationally setting the proportion to screen individuals for the next generation, it effectively balances the diversity and convergence of the population. To verify the performance of the algorithm, comprehensive comparative experiments are conducted between it and seven advanced multimodal multiobjective optimization algorithms. The results show that the proposed algorithm performs excellently in terms of the comprehensive performance in both the decision space and the objective space, significantly outperforming the comparative algorithms.