This Research investigates the effectiveness of spatial grouping technology in geographical situation through spatial clustering algorithms which help to obtain a spatial group with certain features. Due to constraints of traditional spatial group algorithms in the real world, and unique features of every different dataset, it is not feasible to obtain exact spatial groups which are independent of each other. As such, some group may contain dissimilar data. Spatial grouping can be considered as one of the most important unsupervised learning techniques so as every other problem of this kind; it deals with discovering a structure in a collection of unlabeled data. The grouping process is used extensively not only to organize and categorize data, but is also useful for data compression. And it is also useful for analytic model construction and for discovering relevant knowledge in data. This research provides new method to achieve spatial groups for maximum coverage and to avoid overlap. The model is designed with flexibility such that it can support evaluation of each method in the model, in terms of the quality of the resultant groups that is expressed in multi-attributes