Abstract: To solve the problem of unbalanced workload distribution among workstations on a disassembly line, in this study, we developed a multi-objective immune-mechanism cooperative genetic algorithm based on a Pareto set specific to the multi-objective and multi-constraint attributes of the proposed disassembly-line balancing problem model. The proposed algorithm integrates an immune mechanism with the genetic operation and takes the weighted values of the problem characteristics as the construction rules of a vaccine database. Through a set of operations including vaccination, immunoassay, immune balance, and immune selection, the population of individuals gradually moves toward the optimal solution while maintaining population diversity. In addition, we adopt the Pareto set method to achieve cooperative optimization of the multi-objectives, which provides for different priorities of the decision maker. Based on comparison experiments of the population initialization rules, we proved that heuristic rules can generate high-quality initial solutions under a cycle time constraint. Lastly, we confirm the validity and superiority of the proposed algorithm by conducting comparison experiments with several existing algorithms using disassembly cases of different scales.