A practical optimal reliability design of a system requiring high system reliability could be formulated as an appropriate mathematical programming model; however, in the real world, we should be concerned with some kinds of decision criteria. In particular, system reliability and construction cost are basically in conflict with each other, so that when taking both of them into consideration, the system reliability design model can be formulated as a bi-objective mathematical programming model. In this research, we consider a bi-criteria redundant system reliability design problem which is optimized by selecting and assigning system components among different valuable candidates for constructing a series-parallel redundant system. Such a problem is formulated as a bi-criteria nonlinear integer programming (bi-nIP) model. In the past decade, several researchers have developed many heuristic algorithms including genetic algorithms (GAs) for solving multi-criteria system reliability optimization problems and obtained acceptable and satisfactory results. Unfortunately, the Pareto solutions obtained by solving a multi-objective optimization problem using a GA cannot guarantee its quality, and the number of Pareto solutions obtained is sometimes small. In order to overcome such problems, we propose a hybrid genetic algorithm combined with a Fuzzy Logic Controller (FLC) and a local search technique to obtain as many Pareto solutions and as good as possible. The efficiency of the proposed method is demonstrated through comparative numerical experiments. (C) 2006 Wiley Periodicals, Inc.