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The multi-objective optimization strategy called physical programming (PP) provides engineers with a flexible tool to express design preferences with a ‘physical’ meaning. For each objective or specification design, preferences are established through linguistic categories to which numerical values are assigned. In PP, this mapping is made using preference functions as piecewise splines whose curvatures are calculated with an expensive and iterative algorithm. However, mapping between design parameter space and objective space may be largely non-convex and is uninfluenced by the use of gradient-based methods for solving the optimization problem. In this paper, the philosophy of the PP method has been used, but two components have been totally redesigned: a simpler algorithm is used for the construction of preference functions; and the optimizer is replaced by a genetic algorithm that avoids possible …