We investigate how to leverage the notion of motivation in assigning tasks to workers and improving the performance of a crowdsourcing system. In particular, we propose to model motivation as the balance between task diversity–i.e., the difference in skills among the tasks to complete, and task payment–i.e., the difference between how much a chosen task offers to pay and how much other available tasks pay. We propose to test different task assignment strategies: (1) relevance, a strategy that assigns matching tasks, i.e., those that fit a worker’s profile, (2) diversity, a strategy that chooses matching and diverse tasks, and (3) div-pay, a strategy that selects matching tasks that offer the best compromise between diversity and payment. For each strategy, we study multiple iterations where tasks are re-assigned to workers as their motivation evolves. At each iteration, relevance and diversity assign tasks to a worker from an available pool of filtered tasks. div-pay, on the other hand, estimates each worker’s motivation on-the-fly at each iteration, and uses it to assign tasks to the worker. Our empirical experiments study the impact of each strategy on overall performance. We examine both requester-centric and worker-centric performance dimensions and find that different strategies prevail for different dimensions. In particular, relevance offers the best task throughput while div-pay achieves the best outcome quality.