In this talk we propose a new method in order to solve a general blackbox global optimization problem with bound constraints where function valuations are expensive. Our work was motivated by many problems in the oil industry, coming from several fields like reservoir engineering, molecular modeling, engine calibration and inverse problems in geosciences. In such cases, classical derivative free optimization methods often need too many function evaluations, especially in high-dimension cases. To overcome this difficulty, we propose here a new optimization approach, called GOSGrid (Global Optimization based on Sparse Grid), using sparse grid interpolation as surrogate models.