This paper reports on an efficient algorithm for locating the 'optimal' solutions for multi-objective optimization problems by combining a state-of-the-art optimizer with a fitness model-estimate. This hybrid framework is introduced to illustrate how to make sufficient use of an approximate model, which includes a 'controlled' process and an 'uncontrolled' process during the search. With the inclusion of such approximate model in the optimization block, a global reseeding strategy based on previous data is also applied to improve the ability of the multi-objective optimizer to find global set of solutions ('pareto' solutions). To this effect, the popular algorithm, NSGA-II, and a Multi-Layer Perceptron Neural Network (MLP) are combined synergetically to show details of such processing. Furthermore, a simple (but no simpler) method for selecting the 'training' data necessary for eliciting the fitness landscape model is suggested to address what are now a common engineering problems, in particular those associated with sparse data distributions and objectives converging at significantly different speeds. To test the validity of the proposed multi-objective scheme, a series of simulation experiments, using well-know benchmark functions, are conducted and are compared to those carried-out while using the original NSGA-II and SPEA-2, under similar conditions. The proposed method is also applied to the 'optimal' design of alloy steels in terms of chemical compositions and processing conditions and is shown to perform very well.