As a result of the rapid growth of internet and smartphone technology, a novel platform
that attracts individuals and groups known as crowdsourcing emerged. Crowdsourcing is an
outsourcing platform that facilitates the accomplishment of costly tasks that consume long periods
of time when traditional methods are used. Spatial crowdsourcing (SC) is based on location; it
introduces a new framework for the physical world that enables a crowd to complete spatialtemporal tasks. The primary issue in SC is the assignment and scheduling of a set of available
tasks to a set of proper workers based on different factors, such as the location of the task, the
distance between task location and hired worker location, temporal conditions, and incentive
rewards. In the real-world, SC applications need to optimize multi-objectives simultaneously to
exploit the utility of SC, and these objectives can be in conflict. However, there are few studies
that address this multi-objective optimization problem within a SC environment. Thus, the
authors propose a multi-objective task scheduling optimization problem in SC that aims to
maximize the number of completed tasks, minimize total travel cost, and ensure worker workload
balance. To solve this problem, we developed a method that adapts the multi-objective particle
swarm optimization (MOPSO) algorithm based on a proposed novel fitness function. The
experiments were conducted with both synthetic and real datasets; the experimental results show
that this approach provides acceptable initial results. As future work, we plan to improve the
effectiveness of our proposed algorithm by integrating a simple ranking strategy based on task
entropy and expected travel costs to enhance MOPSO performance.