Abstract: This paper proposes a novel algorithm for solving constrained multi objective optimization problems. The proposed approach adopts a new kind of constraint handling method which firstly cut the population with the threshold of constraint violation and then divide the individuals according to the different situations, constraint violation and the objective function values. In addition, a hybrid of differential evolution and immune clonal mechanism is introduced in this paper, in order to search in global by differential evolution and search in local based on the elite by immune clone. The experimental results show that this algorithm compared with NSGA II has good convergence and distribution. Finally, the proposed algorithm is used in the optimization of actual gasoline blending operation, which verify the efficiency of the proposed method, and provide a new way to reduce cost and improve the quality of products for the company.