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For some mechanical design problems a pre-optimization parametric study is commonly conducted (e.g., Gherca, et al., 2013; Shen, et al., 2014). However, a parametric study does not correlate the design factors well for a given objective in the global point of view. For a single-objective optimization problem (SOOP), including the problems being formulated to have a weighted sum of multiple objectives, a single optimal solution is sought. In recent years, multifactor single-objective optimization has been carried out in many fluid-film lubrication studies (Wang, et al., 2000; Wang and Chang, 2002; Zhu and Bogy, 2002; Zhu and Bogy, 2004; Wang and Chen, 2004; Wang, et al., 2009a; Wang, et al., 2009b; Wang, et al., 2013; Senthil Kumar, et al., 2014) to obtain the optimum set of design variables. The weighted sum approach is also effectively applied to solve a bi-objective problem for the sequencing planning of mixed-model assembly line (Shimizu, et al., 2011). Among these studies, the various versions of dividing-rectangle (DIRECT) algorithm (Zhu and Bogy, 2002; Zhu and Bogy, 2004; Wang, et al., 2009) and the particle swarm optimization method (Wang, et al., 2013) have been demonstrated as practical and effective solvers for SOOPs. On the other hand, mechanical design of a component or system usually consists of more than one objective. Even in the case of a design where a single most important objective is desired, the minimization of product cost (a second objective) cannot be ignored in industrial applications. Thus, to effectively solve a problem of two or more objectives is of practical importance. Some studies show the results of recent effort in developing efficient schemes (Wang and Chang, 2004; Hirani and Suh, 2005; Wang, 2005; Bhat and Barrans, 2008; Wang and Cha, 2010; Lu and Xie, 2014) for solving multiobjective optimization problems (MOOPs). To minimize the execution time many of these optimization analyses were carried out by using parallel computing or approximating the computationally intensive function with a surrogate model (Li, et al., 2008; Srirat, et al., 2012). The solution of an MOOP is usually a family of designs, the so-called Pareto optimality solution set or Pareto front. The strategy of solving MOOPs has been very actively developed, due to the problems’ complexity and practical necessity. A few pioneer works for solving multiobjective optimization problems using evolutionary or genetic Multiobjective design of porous air bearing using group inching fortification method