Almost all of the multi-objective optimization evolutionary algorithms(MOEAs) are designed for the Pareto optimal front which is distributed uniformly.But in real world optimizations,the Pareto optimal front usually has a non-uniform distribution.A similar solution set distribution with Pareto optimal front is expected to obtain by decision makers.However,the existing algorithms cannot solve such problems effectively.In this paper,a diversity maintenance method for non-uniformly distributed multi-objective optimization problem(NUDMM) is proposed.In the algorithm,an indicator reflecting 'regular’ degree of distribution-Messy is defined.And a method to decrease Messy of population is designed,which eliminates disordered individual on the condition that the distribution of the Pareto optimal front is unknown.From an extensive comparative study with NSGA-II and SPEA2 on eight non-uniform distribution test problems,it is observed that the proposed method has a good performance in maintaining the real distribution and convergence.