In this paper, a new algorithm is proposed through the clustering-based modeling and genetic algorithms for color-defects detection of ceramic tiles. The algorithm consists of two stages: feature extraction and inspection. In the former phase, the parameters of the algorithms are regulated by using one (some) reference defect-free ceramic - tile image(s) in order to produce the contextual model of colors. Then, during the inspection phase, the colors of the examined ceramic tiles images are compared to the color model; and each difference indicates a color defect. The proposed algorithm integrates a new color clustering algorithm based on min-max criterion and genetic algorithms such that the former is used by the later as the evaluation function. The experimental results on a database including 120 ceramic tiles images categorized to six groups demonstrated acceptable performance in terms of solution quality, computational burden, and adjustable sensitivity.