Many real-world application problems encountered in industry have no analytical formulation, that is they are blackbox optimization problems, and often make use of expensive numerical simulations. We propose a new blackbox optimization algorithm named BOA to solve mixed-variable constrained blackbox optimization problems where the evaluations of the blackbox functions are computationally expensive. The algorithm is two-phased: in the first phase it looks for a feasible solution and in the second phase it tries to find other feasible solutions with better objective values. Our implementation of the algorithm constructs surrogates approximating the blackbox functions and defines subproblems based on these models. The open-source blackbox optimization solver NOMAD is used for the resolution of the subproblems. Experiments performed on instances stemming from the literature and two automotive applications encountered at Stellantis show promising results of BOA in particular with cubic RBF models. The latter generally outperforms two surrogate-assisted NOMAD variants on the considered problems.