In spatial crowdsourcing with multiple types of tasks, participants tend to complete low-cost tasks, which leads to an imbalance in the number of completed tasks of different types, thereby reducing the overall quality of crowdsourcing task completion. To address this issue, this article proposes an incentive mechanism for crowdsourcing competitions based on team collaboration. This mechanism encourages task participants to choose high-cost tasks by calculating differentiated reward schemes for different tasks and combining them with a gamified team ranking competition mechanism, thereby balancing task selection in multi type task. This article models the problem as a two-layer optimization problem, proposing an approximate algorithm for solving Nash equilibrium strategies for inner layer participants and a reward allocation algorithm based on the idea of gradient descent. Finally, this article verified through simulation experiments with various other incentive mechanisms that the proposed incentive mechanism can effectively solve the problem of imbalanced task completion.