Optimal tracking is a widely researched control problem, but the unavailability of sufficient information referring to system dynamics brings challenges. In this paper, an optimal tracking control method is proposed for an unknown launch vehicle based on the global dual heuristic programming technique. The nonlinear system dynamics is identified by an offline trained neural network and a feedforward neuro-controller is developed to obtain the desired system input and to facilitate the execution of the feedback controller. By transforming the tracking control problem into a regulation problem, an iterative adaptive dynamic programming algorithm, subject to global dual heuristic programming with explicit analytical calculations, is utilized to deal with the newly built regulation problem. The simulation results demonstrate that the developed method can learn an effective control law for the given optimal tracking control tasks.