This paper proposes a novel multi-task allocation framework, named PSAllocator, for participatory sensing (PS). Different from previous single-task oriented approaches, which select an optimal set of users for each single task independently, PSAllocator attempts to coordinate the allocation of multiple tasks to maximize the overall system utility on a multi-task PS platform. Furthermore, PSAllocator takes the maximum number of sensing tasks allowed for each participant and the sensor availability of each mobile device into consideration. PSAllocator utilizes a two-phase offline multi-task allocation approach to achieve the near-optimal goal. First, it predicts the participants' connections to cell towers and locations based on historical data from the telecom operator; Then, it converts the multi-task allocation problem into the representation of a bipartite graph, and employs an iterative greedy process to optimize the task allocation. Extensive evaluations based on real-world mobility traces show that PSAllocator outperforms the baseline methods under various settings.