Continuous range queries over moving objects is essential to many location-based services. Aiming at this issue， a distributed search method was proposed for processing concurrent range queries over large scale moving objects. Firstly， formed by a Global Grid Index （GGI） and a local elastic quadtree， a Distributed Dynamic Index （DDI） structure was proposed. Then， a Distributed Search Algorithm （DSA） was proposed based on DDI structure. At the first time， an incremental strategy of updating the query results as objects and query points continuously changed their locations was introduced by DSA. After that， in the incremental update process， a shared computing optimization strategy for multiple concurrent queries was introduced， to incrementally search the range query results of the moving object according to the existing calculation results. Finally， three moving object datasets with different spatial distributions were simulated on the basis of the German road network， and NS （Naive Search）， GI （Grid Index） and Distributed Hybrid Index （DHI） were compared with the proposed algorithm. The results show that compared with DHI， the comparison algorithm with the best performance， DSA decreases the initial query time by 22.7%， while drops the incremental query time by 15.2%， verifying that DSA is superior to the comparison algorithms.