This article studies the control ideas of the optimal backstepping technique, proposing an event-triggered optimal tracking control scheme for a class of strict-feedback nonlinear systems with non-affine and nonlinear faults. A simplified identifier-critic-actor structure of the reinforcement learning algorithm is utilized to achieve optimal control. The identifier estimates the unknown dynamic functions, the critic evaluates the system performance, and the actor implements the control actions, which allow for modeling and control of anonymous systems to achieve optimal control performance. In this paper, a simplified reinforcement learning algorithm is designed by deriving update rules from the negative gradient of a simple positive function related to the Hamilton-Jacobi-Bellman equation, and it also releases the stringent persistent excitation condition. Then, a fault-tolerant control method is developed by applying filtered signals for controller design. Moreover, considering communication resource reduction, an event-triggered mechanism is adopted to design the actual controller. Finally, the feasibility of the proposed scheme is validated through theoretical analysis and simulation.