Quickly and accurately locating an unknown gas emitting source in turbulence is an important research. Previous studies have demonstrated that cognitive strategies can be used to effectively search the source. However, it takes a lot of computation to determine the action by solving the reward function. This paper proposes a fresh searching algorithm named MEGI-taxis, which has a less computational effort than cognitive strategies. It employs the Gaussian Mixture Model (GMM) to extract the information to decide the action instead of the reward function. The area of the Maximum Effective Gaussian distribution (MEGI) determined by the GMM is considered as the most possible area of the source. The searcher guided by the MEGI-taxis algorithm has a move tendency towards the MEGI and it explores the area of the MEGI in square search pattern. The results of experiment demonstrate that the MEGI-taxis algorithm has a better performance than cognitive strategies in mean search time (MST) and success rate (SR). The proposed strategy is also more computationally efficient.