This paper concentrates on the fixed-time optimal consensus issue of multi-agent systems (MASs) under false data injection (FDI) attacks. To mitigate FDI attacks on sensors and actuators that may cause systems to deviate from the reference trajectory, a zero-sum game framework is established, where the secure control protocol aims at the better system performance, yet the attacker plays a contrary role. By solving the Hamilton–Jacobi–Isaacs (HJI) equation related to the zero-sum game, an optimal secure tracking controller based on the event-triggered mechanism (ETM) is obtained to decrease the consumption of system resources while the fixed-time consensus can be guaranteed. Moreover, a critic-only online reinforcement learning (RL) algorithm is proposed to approximate the optimal policy, in which the critic neural networks are constructed by the experience replay-based approach. The unmanned aerial vehicle (UAV) systems are adopted to verify the feasibility of the presented approach.