This paper presents an efficient Verification & Validation (V&V) approach to analyze the robust performance of uncertain AOCS/GNC systems. Current V&V approaches used in industry rely on Monte Carlo campaigns, which require a large number of evaluations on a high fidelity model which is computationally expensive. Instead, this paper suggests replacing this model with a surrogate model based on polynomial chaos expansion (PCE) theory. The polynomial structure enables a very efficient evaluation of the surrogate model. In addition, sensitivity indices and statistical moments (e.g., mean and variance) from the output distribution can be obtained with no extra effort. The application of intrusive and non-intrusive PCE for the probabilistic analysis of AOCS/GNC systems is demonstrated on two real-world satellite missions. The PCE-based results show a very good agreement with Monte Carlo simulations, but require a fraction of the computational time.