Abstract: Team formation problem in social network is gaining prominence in the research field of social network analysis and data mining. Previous study about team formation aimed at finding a team with the lowest communication cost. Some practical applications, such as large-scale software development and large-scale scientific research teams, usually need to divide a task. Based on this requirement, this paper presents a problem named grouping supported team formation in social network, which finds a team of experts to satisfy a complex grouping task and minimize the communication cost. The input of this problem is not a set of keywords of the traditional team formation problem, but a grouping task graph. Meanwhile, we also prove that this problem is NP-hard. Based on team communication models in organizational behavior, we define communication cost criterions for measuring grouping task teams, and propose multiple corresponding greedy searching strategies. The experimental results on real datasets demonstrate that different search strategies are suitable for different communication cost criterions and prove the effectiveness of the proposed algorithm.