Assembly sequence planning (ASP) determines the order of the assembling components of a product. Existing ASP methods have the limitation in planning complex products with high computational costs and inefficient optimization solutions. This thesis applies different variants of Genetic Algorithm (GA) as well as Q-Learning-based Genetic Algorithm (QLGA) to address these challenges. GA, like other metaheuristics, is problem-dependent and typically requires tuning for specific problems. By incorporating Q-learning capabilities into GA, the performance of GA can be enhanced, especially for solving NP-hard ASP problems. Different versions of GA and QLGA with various crossover operators and selection mechanisms are compared to evaluate the proposed approaches through case studies. The results show that QLGA outperforms traditional GA in terms of convergence rate and efficiency. ASP can be significantly improved when Q-learning techniques are combined with traditional GA methods, providing a versatile and efficient solution for product assembly planning.