The advantage of this strategy is its use of the properties of both the surrogate and simulation level to drive optimization. While surrogate level evaluation is much faster than simulation level evaluation, surrogates need simulation points in the region of interest to accurate reproduce the objective function. Since parameter spaces are large and the region of interest is not known a priori, an exhaustive strategy would require a very large number of simulation level evaluations to build globally accurate surrogates, negating the speedup gained by using surrogates. We instead build a surrogate from a minimal initial set of evaluations of the objective function and allow the surrogate to suggest new parameter vectors for the simulation level to evaluate. We can therefore iteratively drive the optimization towards the region of interest without incurring too much computational cost, acquiring more information to improve the surrogates along the way. This allows us to pair the global optimization strategies available at the surrogate level with the accuracy of simulation-level validation.