With the application of the Spatial Information Grid（SIG）

the spatial information managed by SIG being more and more abundant.The abundant spatial information reguires the better application of distributed spatial information query across SIG.However

the remote spatial join queries are always the bottleneck in the distributed spatial information query.Based on this observation

in this paper

the spatial join queries are optimized by taking full advantage of the grid computing resources according to the characteristics of spatial information.At first

the software architecture for distributed spatial query is designed based on the different grid services.The distributed spatial data query software architecture is composed of three different kinds of grid services

namely: Distributed Spatial Data Query Grid Service（DSDQGS）

Spatial Data Grid Service（SDGS） and Remote Spatial Join Query Grid Service（RSJQGS）

These three kinds of grid services cooperate to implement the optimization and execution of the distributed spatial data query.In the architecture

the grid computing resources are utilized by the remote spatial join queries execution grid services.Secondly

the partitioned parallel spatial join queries are implemented by the Kd-Tree spatial partition scheme.In the scheme

an original spatial query is rewrite into several sub-spatial queries bounded by sub regions of the Kd-Tree nodes

which can be run concurrently;therefore

the performance of the remote spatial join queries is improved.The cost model for the partitioned parallel spatial join queries is also presented in paper.The cost of the remote spatial join query involves two parts: the computing cost of the join operation and communication cost of the spatial data.Thirdly

the optimization algorithm for the query planed to generate the remote spatial join queries is designed according to the cost model.The remote spatial join query plan prescribes the way the spatial join query execution

including the scheme for partitioned parallel spatial join query

SDGSs participated in the join query

and assignments of the partitioned parallel spatial join query tasks to RSJQGSs.The cost is benchmark for the remote spatial join query plan.The parameters utilized in the optimization algorithm are managed as properties of the WSRF.The full optimization algorithm is finished when all identified spatial join operators are processed.At last

the future research directions for the optimization of spatial distributed query on SIG are discussed.