As a key research issue, task distribution is a hot and difficult point of current research in the field of mobile crowding sensing(MCS). The method of multi-task distribution participant selection is studied in this paper. According to the historical task completion status of participants, the cumulative reputation and willingness of participants are used to build the quality of service model(QoS). On the basis of maximizing the QoS, the greedy heuristic algorithm is used to solve the participation problem, and the two design schemes including task-centric and user-centric are proposed. The distance constraint factor, integrity constraint factor and reputation constraint factor are introduced into the proposed algorithms. The purpose is to improve the quality of perception tasks as much as possible, so as to improve the final benefits of platforms. Two algorithms are evaluated in the feasibility and effectiveness, and compared with existing algorithms in terms of the number of participants selected, moving distance, and data quality. During the experiment, a step data pricing model is established to quantitatively compare the quality of data uploaded by participants. Experimental results show that the two algorithms proposed are better than the existing algorithms in task quality.