Very often real-world applications involve multiple objectives. Research on multiobjective evolutionary optimization algorithms have amply demonstrated that they are capable of finding multiple and diverse non-dominated solutions which allows the user to choose among many solutions. Although it is difficult to evaluate the importance of the various objectives quantitatively during the conceptual/preliminary stages of the design process, usually qualitative preference can be specified. This paper presents a novel, simple and intuitive way to integrate the user's preference into the evolutionary algorithm. This approach treats relatively more important objectives as adaptive constraints whose ideal values will be adaptively changed during the optimization procedure. Such changes will affect the region feasibility of the objective space which results in the variation of problem type (unconstrained problem, moderately constrained problem or highly constrained problem). As the selection criteria for mating partner depends on the type of problem in the algorithm used here, more selection pressure is put on adaptive constraints. The algorithm is validated using a target matching test problem. The results obtained indicate that the approach can produce good results at reasonable computational costs. The proposed algorithm efficiently guides the population towards the (preferred) region of interest, allowing a faster convergence and a better coverage of the preferred area of the Pareto optimal front based on the relative importance of the objectives.