This paper studies distributed feedback optimization for linear uncertain multiagent systems subject to a class of external disturbances with unknown frequencies. Specifically, the objective is to regulate the outputs of the multiagent system to a common value that minimizes a prescribed global cost function, a sum of local cost functions. A crucial strategy is to develop a distributed optimizer that generates reference signals for the low-level decentralized tracking controllers. For the control synthesis, we use the normal form to derive an internal-model-based control law for the reference tracking and disturbance rejection, and use an adaptive control technique to handle the unknown frequencies of the disturbances. The coupling between the optimizer and the physical control systems is dealt with by a composite Lyapunov function. It is shown that the proposed solution guarantees the boundedness of the closed-loop signals and ensures that the output of each agent converges to the desired minimizer for any initial state. A numerical example demonstrates the effectiveness of the proposed method.