Aiming at the quality and quantity problem of multi-type task completion in spatial crowdsourcing, a method of multi-type task assignment and scheduling was proposed. Firstly, in the task assignment process, by combining with the characteristics of multi-type tasks and users in spatial crowdsourcing and improving the greedy allocation algorithm, a Distance ε based Assignment ( ε-DA) algorithm was proposed. Then the tasks were assigned to the nearby user, in order to improve the quality of task completion. Secondly, the idea of Branch and Bound Schedule (BBS) was utilized, and the task sequences were scheduled according to the size of the professional matching scores. Finally, the best sequence of tasks was found. Due to the low running speed of the scheduling algorithm of branch and bound idea, the Most Promising Branch Heuristic (MPBH) algorithm was presented. Through the MPBH algorithm, local optimization was achieved in each task allocation process. Compared with the scheduling algorithm of branch and bound idea, the running speed of the proposed algorithm was increased by 30%. The experimental results show that the proposed method can improve the quality and quantity of task completion and raise the running speed and accuracy.