With the popularity of mobile devices, Spatial Crowdsourcing (SC) is emerging as a new framework that enables human workers to perform tasks in the physical world. With spatial crowdsourcing, the goal is to outsource a set of spatiotemporal tasks (i.e., tasks with time and location) to a set of workers, which requires the workers to be physically present at the location of the tasks in order to perform them. Research efforts have focused on different aspects of SC, such as task assignment, task scheduling and protecting workers' privacy. However, one of the biggest challenges for the research community is the absence of public real-world datasets from SC applications such as Uber, TaskRabbit, etc. Therefore, we propose a synthetic workload generator for the SC applications, namely SCAWG1, to produce common datasets for experimentation, thus leading to reproducible research. SCAWG facilitates and standardizes workload generation in SC research. Specifically, SCAWG considers realistic spatiotemporal properties and behaviors for both workers and tasks. In addition, to further emulate the real-world workloads, it considers various application-specific constraints and properties of workers and tasks as well as their temporal arrival patterns. SCAWG is very flexible and thus can be customized for different spatial crowdsourcing and crowdsensing applications via a set of user-defined parameters and distributions. We developed an open-source toolbox2 to demonstrate the feasibility and applicability of SCAWG. The toolbox is designed to be generic and extensible, which would expedite experimental studies.