Crowdtesting has emerged as an attractive and economical testing paradigm that features testers from different countries, with various backgrounds and working conditions. Recent developments in crowdsourcing testing suggest that it is feasible to manage test populations and processes, but they are often outside the scope of standard testing theory. This paper explores how to allocate service-testing tasks to proper testers in an ever-changing crowdsourcing environment. We formalize it as an optimization problem with the objective to ensure the testing quality of the crowds, while considering influencing factors such as knowledge capability, the rewards, the network connections, and the geography and the skills required. To solve the proposed problem, we design a task assignment algorithm based on the Differential Evolution (DE) algorithm. Extensive experiments are conducted to evaluate the efficiency and effectiveness of the proposed algorithm in real and synthetic data, and the results show better performance compared with other heuristic-based algorithms.