In neural network optimization, multiple goals and constraints cannot be handled independently of the underlying optimizer. While "better" solutions should be rated higher than "worse" ones, the resulting cost landscapes must also comply with requirements such as continuity and differentiability of the cost surface. The genetic algorithm (GA), which has found application in many areas not amenable to optimization by other methods, is a random search technique which requires the assignment of a scalar measure of quality, or fitness, to candidate solutions. This paper proposes that the fitness assignment be interpreted as, or at least related to, a multicriterion decision process. A suitable decision-making framework, based on goals and priority, is subsequently formulated in term of fuzzy reasoning and shown to encompass a number of simpler decision strategies. Since the GA is a random search process and therefore takes more time to find a solution in the problem domain, a proper search direction is required in order to produce an optimum result. Fuzzy logic cannot provide an exact solution but can be used as a useful tool for reasoning. In this paper, the reasoning capability of fuzzy logic is used to provide a proper direction for genetic search in a problem domain and thus to achieve faster convergence in the GA. The effectiveness of this is shown in neural network optimization applied to dynamic modelling of an experimental flexible manipulator. The results show that the new fuzzy logic approach is superior to conventional exploration of the genetic search region.