The belief-desire-intention (BDI) model can solve the problem of reasoning and decision-making of agents in a particular environment, but lacks the ability of decision-making and learning in dynamic and uncertain environments. Reinforcement learning solves the decision-making problem of agent in unknown environments, but lacks the rule description and logical reasoning of the BDI model. Aiming at the strategic planning problem of the BDI in the unknown and dynamic environment, we propose an optimal strategy planning method based on Q-learning algorithm of reinforcement learning. And we make  improvement for the decision-making mechanism on the implementation model of the BDI—agent speak language (ASL). Finally, the simulation of the maze on the ASL simulation platform Jason proves the feasibility of this method, and the new agent model can fulfill tasks in uncertain environments.