Support for region queries is crucial in geographic information systems, which process exact queries through spatial indexing to filter features and subsequently refine the selection. Although the filtering step has been extensively studied, the refinement step has received little attention. This research builds upon the QR-tree index, which decomposes space into hierarchical grids, registers features to the grids, and builds an R-tree for each grid, to develop a new QRB-tree index with two levels of optimization. In the first level, a bucket is introduced in every grid in the QR-tree index to accelerate the loading and search steps of a query region for the grids within the query region. In the second level, the number of candidate features to be eliminated is reduced by limiting the features to those registered to the grids covering the corners of the query region. Subsequently, an approach for determining the maximal grid level, which significantly affects the performance of the QR-tree index, is proposed. Direct comparisons of time costs with the QR-tree index and geohash index show that the QRB-tree index outperforms the other two approaches for rough queries in large query regions and exact queries in all cases.