The article presented a Nearest Neighbor Index Fuzzy Clustering (NNI-FC) algorithm for ecological regionalization based on multi-layer grid model with consideration of spatial distribution of geographical factors such as climate, vegetation and topography. It's a "bottom-up" regionalization approach and solved the problem of how to determine the ecological regionalizations type and its boundary by calculating the Nearest Neighbor Index (NNI) and the similarity between grids. Numeric and non-numeric features were considered simultaneously in the NNI and similarity, so the algorithm integrated both qualitative and quantitative regionalization methods. The algorithm consisted of three consequence steps: data preprocessing, core region generation and fragmented region elimination. In the data preprocess, some numeric property values were transformed to non-numeric ones through classification of the combination of geographical factors. Next, core regions and fragmented regions were generated by ROCK algorithm, which clustering the adjacent discrete grids with the same property values. Then, on the basis of analyzing the fragmented regions area coverage and its spatial distribution by using NNI, the algorithm divided the fragmented regions into small pieces and merged them into the core region which has the biggest similarity. Finally, an eco-regionalization scheme for the given natural section is formed. The experiment of ecological regionalization for North of Xinjiang shows that the algorithm achieves over 80% classification accuracy and can be very good at expressing the diversity of regional characteristics. Besides, different levels of eco-regionalization scheme can be obtained by adjusting the thresholds of the algorithm and its time complexity is between linear and quadratic ones depending on the thresholds.