Abstract: Evolutionary multi-objective optimization (EMO) has become a very popular topic in the last few years. However, to design an efficient and effective EMO algorithm to find the near-optimal and near-complete Pareto front is a challenging task. In this paper, a novel differential evolution algorithm is proposed to solve multi-objective optimization problems (MOPs) efficiently. The proposed approach uses an archive population to retain the obtained non-dominated solutions; also it adopts the orthogonal design method with quantization technique to generate an initial population of points that are scattered uniformly over the feasible solution space, so that the algorithm can evenly scan the feasible solution space to locate good points for further exploration in subsequent iterations. Moreover, it is based on the ε-dominance concept to obtain a good distribution of Pareto-optimal solutions in a small computational time. To make the algorithm converge faster, the new approach employs a hybrid selection mechanism in which a random selection and an elitist selection are interleaved. Experiments on eight benchmark problems of diverse complexities show that the new approach is able to obtain a good distribution in all cases. Compared with several other state-of-the-art evolutionary algorithms, it achieves not only comparable results in terms of convergence and diversity metrics, but also a considerable reduction of the computational effort. Furthermore, the influences of different CR value and the parameter value of hybrid selection mechanism on the performance of the algorithm are discussed experimentally.