Abstract: Two optimization objectives, minimizing the variation in crucial parts consumption and the makespan, are simultaneously considered to study the sequencing problems in car engine mixed-model assembly lines under limited intermediate buffer size constraints. The mathematical models are presented. Since the problem addressed is NP-hard, a multi-objective genetic algorithm is proposed, in which hybrid crossover operators and a heuristic mutation method are adopted, and the Pareto ranking method and the sharing function method are employed to evaluate the individuals′ fitness. The optimization result of the multi-objective genetic algorithm is compared with that of a multi-objective annealing algorithm. The comparison result illustrates that the multi-objective genetic algorithm proposed outperforms the multi-objective annealing algorithm in respect of the solutions′ desirability and also the computation efficiency. The multi-objective genetic algorithm is an efficient algorithm for solving the sequencing problem in mixed-model assembly lines under buffer size constraints.