The earlier concurrency problems are identified, the less costly they are to fix. As larger, more complex concurrent systems are developed, early detection of problems is made increasingly difficult. Meant to be used in the context of Model Driven Development, we have developed a general approach, based on the analysis of design models expressed in the Unified Modeling Language (UML) that uses specifically designed genetic algorithms to detect concurrency problems. All relevant concurrency information is extracted from systems’ UML models that comply with the UML Modeling and Analysis of Real-Time and Embedded Systems profile. Our approach was previously shown to work for both deadlocks and starvation. The current paper addresses data race detection, further illustrating how our approach can be tailored to other concurrency issues. Our main motivations are (1) to devise practical solutions that are applicable in the context of UML design of concurrent systems without requiring additional modeling and (2) to achieve scalable automation. Results on a case study inspired from the Therac-25 radiation machine show that our approach is effective in the detection of data races.