In order to make the operator selection more efficient in multi-objective evolutionary algorithms(MOEAs) with multiple operators, this paper proposes a MOEA based on the decomposition with double credit assignment(DCA-MOEA/D). Firstly, the operator pool in proposed algorithm consists of two existing operators and two variants of differential evolution(DE) based on the direction-guided search strategy. Individuals use a roulette wheel-like process to pick up an operator to generate offspring at each generation. Subsequently, the credit value of each operator is determined by combining two credit assignment methods according to the performance of the offspring, and the selection probability of each operator is updated by the credits. Meanwhile, an extra archive is defined and the dominated sorting and crowded distance strategies are used to maintain it. Finally, the whole evolutionary process is divided into 5 steps to achieve the balance between exploration and exploitation in operator selection. Empirical study validates the effectiveness of the proposed algorithm through the contrast experiment with four MOEAs in terms of IGD and HV value on 23 benchmark problems.