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In this paper, a differential evolution algorithm with Q-Learning (DE-QL) for solving engineering Design Problems (EDPs) is presented. As well known, the performance of a DE algorithm depends on the mutation strategy and its control parameters, namely, crossover and mutation rates. For this reason, the proposed DE-QL generates the trial population by using the QL method in such a way that the QL guides the selection of the mutation strategy amongst four distinct strategies as well as crossover and mutation rates from the Q table. The DE-QL algorithm is well equipped with the epsilon constraint handling method to balance the search between feasible regions and infeasible regions during the evolutionary process. Furthermore, a new mutation operator, namely DE/Best to current/l, is proposed in the DE-QL algorithm. In this paper, 57 EDPs provided in “Problem Definitions and Evaluation Criteria for the CEC …