Evolutionary Multi-objective Optimization is one of the most important researches in multi-objective optimizations. The number of bisections of the space in the adaptive grid algorithm is difficult to be established. If the number is not chosen appropriately, it will make a poor convergence and a bad diversity of solutions set. A novel multi-objective evolutionary based on Hybrid Adaptive Grid Algorithm (HAGA) is presented in this paper. It is made up of a local search operator and a pruning operator, and then combined with differential evolution operator. On one hand it improves the convergence of the algorithm; on the other hand it can improve the spread and the distribution of the solutions set. From an extensive comparative study with three states-of-the-art algorithms on four test problems, it is observed that the proposed algorithm outperforms the other three algorithms as regards convergence and comprehensive performance.