This article reviews the major improvements in efficiency and quality of evolutionary multi-objective and multi-disciplinary design optimization techniques achieved during 1994–2021. At first, we introduce briefly Evolutionary Algorithms (EAs) of increasing complexity as accelerated optimizers. After that, we introduce the hybridization of EAs with game strategies to gain higher efficiency. We review a series of papers where this technique is considered an accelerator of multi-objective optimizers and benchmarked on simple mathematical functions and simple aeronautical model optimization problems using friendly design frameworks. Results from numerical examples from real-life design applications related to aeronautics and civil engineering, with the chronologically improved EAs models and hybridized game EAs, are listed and briefly summarized and discussed. This article aims to provide young scientists and engineers a review of the development of numerical optimization methods and results in the field of EA-based design optimization, which can be further improved by, e.g., tools of artificial intelligence and machine learning.