This study proposes an efficient evolutionary algorithm, Intelligent Genetic Algorithm (IGA), for inference of S-system models of large-scale genetic networks from the observed time-series data of gene expression patterns. High performance of IGA mainly arises from an intelligent crossover operation which applies orthogonal experimental design to speed up the search by using a systematic reasoning method instead of the conventional generate-and-go method. The proposed intelligent crossover employs a divide-andconquer technique to cope with the problem of a large number of S-system parameters. The effectiveness of IGA is evaluated using simulated expression patterns. The proposed IGA with an existing problem decomposition strategy can efficiently cope with the inference problem of S-system models with several dozen genes to significant accuracy using a single- CPU personal computer.