<p>The development of network equipment and sensing technology has led to the increasing popularity of spatial crowdsourcing (SC). This paper addresses the spatial task allocation problem in the SC system, where workers perform spatial tasks allocated to them at the designated location. Existing studies typically focus on one worker who can only perform one task type at a time. Unfortunately, this approach significantly reduces the efficiency of workers’ output in practical applications. Hence, we propose a multi-type adaptability service model wherein a worker can simultaneously perform multiple types of tasks. Considering mutually exclusive task types, we propose a compatible allocation of task types. Furthermore, we propose a work capacity that enables a worker to perform multiple tasks simultaneously within the work capacity limits. An auction-based pricing model is proposed to provide scalable services and maximize the value of spatial tasks. We propose an efficient allocation mechanism and show that it achieves strategy-proof and group strategy-proof. In addition, the proposed mechanism achieves voluntary participation, individual rationality, consumer sovereignty, and budget balance. Moreover, the approximation of the proposed mechanism is analyzed. Finally, we use comprehensive simulations to verify the performance of the proposed mechanism.</p>