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Global optimization is a widely used technique that finds application in many sciences such as physics, economics, medicine, etc., and with many extensions, for example, in the area of machine learning. However, in many cases, global minimization techniques require a high computational time and, for this reason, parallel computational approaches should be used. In this paper, a new parallel global optimization technique based on the differential evolutionary method is proposed. This new technique uses a series of independent parallel computing units that periodically exchange the best solutions they have found. Additionally, a new termination rule is proposed here that exploits parallelism to accelerate process termination in a timely and valid manner. The new method is applied to a number of problems in the established literature and the results are quite promising.