Similarity search is a very important problem in data mining. It retrieves similar objects in database and finds proximity between objects. It can be applied to image/picture databases, spatial databases, and time-series databases. For Euclid space (a special metric space), similarity search algorithms based on R-tree are efficient in low-dimensional space, but degenerate into linear scan for high-dimensional space. This phenomenon is called dimensionality curse. This paper presents a new partition and index method of metric space, rgh-tree which distributes and partitions objects by using distance information if objects with rew fixed reference. It produces a balance tree with no data overlay. In addition, an algorithm based on rgh-tree, which is suitable for similarity search in metric space, is presented in this paper. The algorithm overcomes the shortcomings of the exiting algorithms, which has less I/O cost and times of computing distance, with average complexity o(n^0.58).