The current global unpredictable market is characterized by increasing demand for highly customized products. To thrive in this scenario, it becomes essential to establish a closer interaction between product and manufacturing system, keeping the main focus on the client. Modular product design (MPD) is the best strategy to produce a large product variety. MPD’s configuration phase represents a key step for mass customization because it allows customers to be integrated into the value-creation process. Reconfigurable manufacturing systems (RMS) seem to be the most appropriate manufacturing system to manufacture mass-customized products due to their ability to be quickly reconfigured, adjusting their production capacity and functionality to fit new market demands. Aiming to integrate individual customer needs with product and manufacturing decisions, this paper proposes a new 0-1 nonlinear integer programming model to concurrently optimize the configuration of modular products and RMS (including machine and layout configuration levels), driven by individual customer requirements. A genetic algorithm-based approach is proposed to solve this model, and its parameters are tuned with a 2-full factorial design. An example of customizable office chairs is used to illustrate the proposition, and different scenarios of customer requirements and RMS configurations were presented. Results evidence that varying initial machines’ configurations can highly affect the process plan and the overall manufacturing costs; however, there is no evidence that changes in initial layout configurations cause significant effects. In summary, this work confirms the relevance of integrating modular product and RMS configuration decisions for minimizing costs of producing highly mass-customized products.