<p>This article presents an innovative method for the efficient capture of the Pareto front in the context of multiobjective optimization applied to mechanical design, specifically for a computer numerical control (CNC) milling machine. The primary objective is to develop an approach that overcomes the limitations of existing methods in terms of convergence, solution diversity, and constraint management. The data comes from a case study on the design of a CNC machine, involving several conflicting criteria such as manufacturing cost, structural rigidity, and cutting performance. To achieve this goal, the article employs a method based on a hybrid evolutionary algorithm, combining advanced constraint handling techniques and metamodels to optimize performance. The results demonstrate that the new method is capable of generating a diverse set of non-dominated solutions, thereby representing a well-defined Pareto front. This approach offers significant implications for the mechanical industry, particularly in supporting decision-making, accelerating design processes, and fostering innovation. In conclusion, the article recommends the adoption of this method in other mechanical design contexts to enhance the efficiency and relevance of proposed solutions, while also opening new research perspectives for further optimization of multiobjective processes.</p>