A new concept of the optimization problem under uncertainty is proposed and treated in the paper. It is an extension of fuzzy optimization in which the degrees of rejection of objective(s) and of constraints are considered together with the degrees of satisfaction. This approach is an application of the intuitionistic fuzzy (IF) set concept to optimization problems. An approach to solving such problems is proposed and illustrated with a simple numerical example. It converts the introduced intuitionistic fuzzy optimization (IFO) problem into the crisp (non-fuzzy) one. The advantage of the IFO problems is twofold: they give the richest apparatus for formulation of optimization problems and, on the other hand, the solution of IFO problems can satisfy the objective(s) with bigger degree than the analogous fuzzy optimization problem and the crisp one. (c) Elsevier