Salinity is one of the main problems in agriculture, negatively influencing the survival, biomass production, and yield of food crops. Exposure to high salinity is connected with ionic stress due to accumulation of sodium ions, osmotic stress, and reactive oxygen species production. To develop crop plants with enhanced tolerance of saline stress, a basic understanding of physiological, biochemical and gene regulatory networks (GRN) is essential. In this paper, an approach to study the saline stress response and tolerance of plants through the GRN involved in this process is proposed. In particular, we reconstruct the GRN of Ara-bidopsis thaliana saline stress response using genetic algorithms and a Boolean network model. The proposed computational intelligence approach was able to successfully infer 1000 threshold Boolean networks that contained the desired Boolean trajectory. The inferred networks were used to build a consensus network, which was useful to identify the regulations or interactions among the genes that were more plausible.