Neighborhood multi-granulation rough set model has captured more and more attention, due to its superior performance on dealing with heterogeneous data, including categorical attributes and numerical attributes, from the perspective of multi-granularity and multi-level. To effectively reduce the iterations in computing attribute reduction and realize the quick attribute reduction algorithm, the effect on positive region, caused by different attribute subsets and different neighborhood radiuses, is deeply analyzed based on the double granulate criterion. Considering the monotonicity of positive region with the joint function of attribute subset and neighborhood radius, a quick attribute reduction algorithm of neighborhood multi-granulation rough set model based on the double granulate criterion is developed. The theoretical analysis and comparable experiments, verify the effectiveness and superiority of the proposed algorithm.