The collaboration problem in social networks has attracted lots of interests among the data mining community. Previous work focused on finding a team with the lowest communication cost to complete all tasks in a project. However, tasks in the realistic projects usually have partial ordering relations. Existing methods cannot deal with partial ordering relations, and thus are not capable of obtaining an effective team. In this paper, we study how to complete the tasks with partial ordering relations effectively, and propose a novel collaboration problem in social networks, named CSN-TPR (collaboration problem in social networks based on tasks with partial ordering relations). Specifically, we investigate how to select an appropriate team in social networks to complete the tasks with partial ordering relations so as to minimize the total cost which is composed of communication cost, time cost and budget cost. We firstly prove that CSN-TPR is NP-hard, and then adopt hill-climbing method, branch and bound strategy and dynamic programming method to propose an approximation algorithm called HillClimbingTF_BBS. HillClimbingTF_BBS can not only acquire an effective team, but also obtain the task allocation of each team member and the start time of each task. The experimental results on real data show that HillClimbingTF_BBS can solve CSN-TPR effectively and efficiently.