<p>Nowadays, workers’ age, gender, and health are often overlooked in job assignments, leading to frequent occupational accidents and musculoskeletal disorders (MSDs). To address this, the present study proposes a job–worker assignment model that incorporates ergonomic constraints, specifically workers’ maximum load capacity based on age and gender. The model aims to minimize the gap between assigned job loads and workers’ capacity, thereby improving both safety and efficiency. The problem was solved using genetic algorithm (GA) and particle swarm optimization (PSO) for a case with ten workers and 15 jobs. A penalty function was integrated into both models to discourage unassigned jobs, idle workers, and cases where workers exceeded their load limits. This mechanism transformed the task into a more complex optimization problem while ensuring that health and ergonomic considerations were respected. The results showed that worker capacity utilization reached 86.81% with GA-based assignments and 85.53% with PSO-based assignments. These outcomes demonstrate that GA produced slightly better workload distribution compared to PSO in this case. The model’s primary objective is effective allocation of tasks while protecting workers’ health. By incorporating ergonomic constraints, the system enhances efficiency and prevents health risks. Unlike classical assignment problems, this approach accounts for individual differences, resulting in more realistic and precise job allocations. Overall, the proposed model provides a healthier and more balanced distribution of workload. It demonstrates how ergonomic considerations can be integrated into workforce planning and has strong potential as a decision-support tool in occupational management.</p>