Optimization of expensive computer models with the help of Gaussian process emulators is now commonplace. However, when several (competing) objectives are considered, choosing an appropriate sampling strategy remains an open question. We present here a new algorithm based on stepwise uncertainty reduction principles. Optimization is seen as a sequential reduction of the volume of the excursion sets below the current best solutions (Pareto set), and our sampling strategy chooses the points that give the highest expected reduction. The method is tested on several numerical examples and on an agronomy problem, showing that it provides an efficient trade-off between exploration and intensification.