Differential evolution (DE) is a simple yet powerful evolutionary algorithm for global numerical optimization. Different strategies have been proposed for the offspring generation; but the selection of which of them should be applied is critical for the DE performance, besides being problem-dependent. In this paper, the probability matching technique is employed in DE to autonomously select the most suitable strategy while solving the problem. Four credit assignment methods, that update the known performance of each strategy based on the relative fitness improvement achieved by its recent applications, are analyzed. To evaluate the performance of our approach, thirteen widely used benchmark functions are used. Experimental results confirm that our approach is able to adaptively choose the suitable strategy for different problems. Compared to classical DE algorithms and to a recently proposed adaptive scheme (SaDE), it obtains better results in most of the functions, in terms of the quality of the final results and convergence speed.