Concerning low efficiency and accuracy of the ridesharing user group discovery in shared transportation environment, a GeoOD-Tree index was established based on R-tree principle, and a group discovery strategy to maximize the multiplying rate was proposed. Firstly, the feature extraction and calibration processing of original spatio-temporal trajectory data was carried out to mine effective Origin-Destination (OD) trajectory. Secondly, a data structure termed GeoOD-Tree was established for effective storage management of OD trajectory. Finally, a group discovery model aiming at maximizing ridesharing travel was proposed, and a pruning strategy using by K Nearest Neighbors (KNN) query was introduced to improve the efficiency of group discovery. The proposed method was evaluated with extensive experiments on a real dataset of 12000 taxis in Xi'an, in comparison experiments with Dynamic Time Warping (DTW) algorithm, the accuracy and efficiency of the proposed algorithm was increased by 10.12% and 1500% respectively. The experimental results show that the proposed group discovery strategy can effectively improve the accuracy and efficiency of ridesharing user group discovery, and it can effectively improve the rideshared travel rate.