One issue with optimization is that when the problem becomes more complicated, the stand-alone optimizer is unable to obtain the global optimal value consistently. That is why the Inner-Outer Array is developed to help the optimizer find a global optimum without going too deeply into the optimizer's parameter settings, which are not always applicable. As a result, this paper presents a novel hybridization approach combining Inner-Outer Array (IOA) and Genetic Algorithm (GA). IOA is a critical step in the IOA-GA method since it aids in the discovery of the global or near-global optimal solution. The developed approach, known as the Inner-Outer Array (IOA), is based on two stages of experimental design: parameter design and tolerance design. Depending on the number of variables and constraints vs. problem size, this approach has one inner array and one or more outer arrays. During the preceding few decades, GAs have proven to be an effective technique for solving real-world optimization problems. In the case of a wide solution space and multiple local optima, however, GAs cannot guarantee a global optimum solution. Here comes the role of the exploratory ability of IOA in scoping the search space, including guiding GA to reach the global or near global optimal result, which is the purpose of this work. More than 15 complex engineering optimization applications, inspired by real problems in the field of mechanical engineering, are used to verify the performance of the proposed method IOA-GA. This research paper used two issues from the literature.