To improve the disassembly efficiency and reduce disassembly energy consumption, the robotic parallel disassembly mode was introduced in the disassembly sequence planning problem, a robotic parallel disassembly sequence planning model was constructed, and a genetic algorithm based on reinforcement learning was designed. To verify the correctness of the model, a mixed integer linear programming model was constructed. In the algorithm, a goal-oriented encoding and decoding strategy was constructed to improve the quality of the initial solution. Q learning was used to select the best crossover and mutation strategies in the iteration process to enhance the algorithm's adaptability. Finally, in an engine disassembly case with 34 tasks, the superiority of the proposed algorithm was verified by comparing with four classic multi-objective algorithms. The analysis of the disassembly schemes shows that the robotic parallel disassembly mode can effectively shorten the completion time and reduce disassembly energy consumption.