Many colour constancy algorithms have been proposed to achieve a good performance in this ﬁeld. The gamut mapping algorithm is one of the most accurate and promising algorithms based on gamut assumption: illuminant can be estimated by comparing the colours distribution in the current image to acanonical gamut (i.e., a pre-learned distribution of colours). However, the gamut mapping algorithm is precise but it is time consuming. Therefore, some other methods such as GCIE (gamut constrained illuminant estimation) and CGM (cubical gamut mapping) have been proposed which work faster. However, the results of such methods are poor when the source light is not in the pre-deﬁned ones. In this paper, we propose an evolutionary algorithm for colour constancy based on gamut mapping assumption. This approach overcomes the mentioned problem in other gamut-based methods. The proposed evolutionary method uses a simple chromosome structure together with simple operators such as mutation, selection, and reproduction. Two versions of the proposed methods have been presented here. The ﬁrst one works on image pixels, while the second one tries on image derivative. The experiments were done on three different data sets that are used in literature and results were satisfactory. The results showed that the proposed method is much improved when compared to other related methods in most of the time especially in the case of real world images.