Robust analysis and optimization is typically based on repeated calls to a deterministic simulator that aim at propagating uncertainties and finding optimal design variables. Without loss of generality a double set of simulation parameters can be assumed: x are deterministic optimization variables, u are random parameters of known probability density function and f (x, u) is the objective function attached to the simulator. Most robust optimization methods involve two imbricated tasks, the u's uncertainty propagation (e.g., Monte Carlo simulations, reliability index calculation) which is recurcively performed inside optimization iterations on the x's. In practice, f is often calculated through a computationally expensive software. This makes the computational cost one of the principal obstacle to optimization in the presence of uncertainties.