Traditional aircraft scheduling on carrier flight deck relies heavily on human commander decisions. To improve the computer aided decision making, an inverse reinforcement learning method was proposed. Learning from the commander or expert's demonstration, a Markov decision process (MDP) based aircraft scheduling model by analyzing the aircraft operations on deck was proposed. Then, the optimal policy and schedule were generated by using the linear approximating and inverse reinforcement learning method. Simulation results show that our method can learn expert's demonstration well, satisfy the requirement of scheduling optimization, and facilitate the computer aided decision making.