This paper proposes to design an X-bar control chart through an integrated three-stage multi-objective optimization process. The multi-objective formulation reflects the needs of X-bar control chart designing process’s multiple objectives, e.g., the expected time that the process remains in statistical control status, the type I error, and the detection power. The optimization process starts with many-objective NSGA-II (NSGA-III), which is a multi-objective evolutionary algorithm (MOEA), to search the Pareto frontiers. Then, Data Envelopment Analysis model (DEA) is applied to find the efficient optimal solutions. After obtaining a manageable size of efficient solutions, a popular Multiple Criteria Decision Making (MCDM) technique, known as VIKOR, is applied to rank the optimal solutions. The proposed multi-stage multi-objective optimization process is applied in a case study and the outcomes are compared with the results in the literature. The comparison results reveal that the proposed multi-stage optimization process introduces more ranked practical efficient solutions.