A three phases shuffled frog leaping framework for multi objective combinatorial optimization is proposed. The framework adopts the idea of phasing and modularization, and divides the evolutionary process of population into three phases: rapid convergence, exploration and expansion, exploit extremum. In the rapid convergence phase, the Pareto front is quickly positioned so that the whole population quickly gathers near the front. In the exploration and expansion phase, the accuracy of the solution is further improved and the population is evenly distributed on the front. In the exploit extremum phase, the boundary extremum on each objective is searched to enhance the distribution performance. For different modules in different stages, different strategies are adopted to improve the solution performance of the framework. The proposed framework has good generality for multi-objective combinatorial optimization problems. When solving different types of problems, only the corresponding coding mode, individual generating operator and constraint processing mechanism need to be designed. The classical multi-objective knapsack problem is used as the test problem, and compared with five existing algorithms, the results show that the proposed framework has good performance, and the hybrid frog hop algorithm based on the framework has better convergence and distribution.