In many engineering application the need arises to solve multi-objective optimization problems that involve computationally expensive functions. Our approach for multiobjective optimization is Normal-Boundary Intersection (NBI) by Das and Dennis. We can run NBI either on surrogate models of the simulations or directly on the simulations. Neither of these alternatives is effective on complex functions; the surrogates are not accurate enough, and using the simulations directly is too costly. We have found a unique approach that solves this dilemma. Our approach combines the use of surrogates and simulations in a way similar to the general surrogate management framework by iteratively using both the surrogate models and the simulations.