The worker is an essential component that plays a key role in the overall operation of manufacturing systems. In the context of Industry 5.0, there is growing interest in factors related to sustainable production, such as worker well-being and the working environment. A human-centered approach helps to ensure worker resilience by analyzing worker characteristics and incorporating them within manufacturing activities. However, finding solutions that satisfy both productivity and human factors can be challenging due to the complex relationship between these aspects. This study proposes a multi-objective optimization methodology for worker assignment in manual assembly production lines using a human-centered approach and a genetic algorithm. First, process-specific worker characteristics related to productivity and resilience are analyzed using digital human models and then integrated into the optimization problem. The genetic algorithm is applied to derive a non-dominated Pareto front, providing multiple alternative solutions. A case study was conducted in a laboratory environment simulating a home appliance assembly line, verifying the validity of the proposed methodology. The novelty of this study lies in capturing both productivity and worker resilience characteristics through digital human models and addressing the trade-off between these attributes using a genetic algorithm. The genetic approach maintains a diverse solution set, facilitating the reflection of user preferences in the decision-making phase. This study aims to serve as a reference for the operation and optimization of human-centered manufacturing systems.