Aiming at the deficiency of current multi-mode multi-objective optimization algorithm in obtaining the integrity and convergence of Pareto solution set, a decision space self-organizing multi-mode multi-objective whale optimization algorithm (MMO_SOM_WOA) is proposed. First, whale optimization algorithm is used to solve multi-modal multi-objective problems for the first time, and the ability to find the integrity of Pareto solution set is improved through the randomness of whale optimization algorithm itself. Secondly, the self-organizing mapping network is combined with whale optimization algorithm to establish a good neighborhood for whale optimization algorithm at the beginning of iteration. Finally, the elite reverse learning strategy is used to initialize the population and non dominated sorting mechanism to obtain uniform and complete solutions. Through simulation comparison with the current five classical algorithms on multi-modal multi-objective optimization problems, the results show that MMO_SOM_WOA algorithm takes into account both the diversity of Pareto solution set and the integrity of Pareto solution. The convergence speed and convergence accuracy are improved and have high performance, effectively solving multi-modal and multi-objective optimization problems.