Computer vision-based wildfire detection is a crucial task for enabling a rapid firefighter response, allowing to mitigate several damages in ecosystems and economy. Moreover, wildfire segmentation methods allow for determining the precise position where the fire is present within the image. A number of rule-based models have been proposed since they are human-readable. Moreover, the color content is the most utilized feature to characterize the fire pixels. Generally, rule-based models determine fire pixels in a binary manner, making it difficult to handle the uncertainty present in the data. In this research work, we propose a system for automatically calculating color-based rules using fuzzy operators to determine the fire pixel. Specifically, we propose to use calculated color-based features. In contrast to other rule-based models, which perform fire segmentation using inequalities, our approach constructs a fire segmentation model by handling uncertainty in a fuzzy approach. By using a genetic approach, the proposed system searches for the best ensemble of fuzzy rules to form the model. According to the results obtained, the proposed model outperforms the state-of-the-art models in various segmentation metrics.