In order to improve the solve performance of multi-objective evolutionary algorithms, a heuristic hybrid framework for evolutionary multi-objective optimization combining the global search and local search based on populations is proposed. The framework adopts modular, systematic design thoughts and different strategies can be used in different modules to form different algorithms. The classic non-dominated sorting genetic algorithm II(NSGA-II) and multi-objective evolutionary algorithm based on decomposition(MOEA/D) algorithm are used as the algorithm of evolutionary algorithm module to verify the effectiveness of the proposed hybrid framework. The numerical experiments show that, the proposed hybrid framework has good performance, which can achieve a balance between diversity and convergence of algorithm and effectively improve the solve performance of existing multi-objective evolutionary algorithms.