This paper explores distributed optimization strategies that achieve fixed-time convergence in multi-agent systems under external disturbance conditions. Given specific assumptions, our proposed protocol ensures that multi-agent systems can simultaneously achieve disturbance rejection, consensus, and cost function minimization, all within a fixed-time. Additionally, the stability of the MASs is rigorously demonstrated using the Lyapunov function, providing a theoretical foundation for the protocol’s effectiveness. At last, a numerical simulation is given to verify the validity and rationality of the model.