Human collaboration with robots and machines in assembly lines is now widespread in the industry. Ergonomics research is vital for optimizing labor productivity. Repetitive tasks in assembly lines can cause worker fatigue and strain. Thus, scheduling work hours and breaks according to human physiology is crucial. This study employs a multi-adaptive genetic algorithm to enhance worker activity schedules, ensuring a balance in energy consumption while maintaining stable assembly line operations. The objective is to create efficient schedules guiding workers to collaborate effectively with robots and machines. An actual case study involves a bulb assembly system utilizing UR3 robots, managing tasks like bulb reception, assembly, and product delivery to the output line and warehouse. The study’s findings demonstrate the effectiveness of the multi-adaptive genetic algorithm in improving worker activity schedules.