<p>Selecting configurations for Reconfigurable Manufacturing Systems (RMS) is challenging and crucial for advancing RMS to a more adaptable and efficient state. This challenge arises because selecting machine configurations for part operations involves multiple, often conflicting, objectives. In this study, a multi-objective optimization framework is developed for the Reconfigurable Single Product Process Plan (RSPPP) and demonstrated through a detailed case study. Since such problems are combinatorial and complex, the Non-Dominated Sorting Genetic Algorithm-III (NSGA-III), which is well-suited for higher-dimensional objectives, is used to generate Pareto-optimal solutions. Subsequently, the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) is applied to rank and prioritize these solutions. The proposed NSGA-III–TOPSIS framework improved average machine utilization by 7.8%, reduced total cost by 5.4%, and consistently selected the optimal configuration across all sensitivity scenarios, demonstrating the robustness and practical relevance of the approach. The framework evaluates four key objectives—cost, operational capability, machine reconfigurability, and machine utilization—to support effective selection of Reconfigurable Machine Tool (RMT) configurations. The findings highlight both the significance and complexity of multi-objective optimization in RMS, offering a valuable reference for researchers and practitioners aiming to enhance system performance and adaptability.</p> Graphical Abstract <p></p>