In modern parallel and distributed systems, the time for exchanging data is usually larger than that for computing elementary operations. Consequently, these communications slow down the execution of the application scheduled on such systems. Accounting for these communications is essential for attaining efficient hardware and software utilization. Therefore, we provide in this paper a new combined approach for scheduling parallel applications with large communication delays on an arbitrary number of processors. In this approach, a genetic algorithm is improved with the introduction of some extra knowledge about the scheduling problem. This knowledge is represented by a class of clustering algorithms introduced recently, namely, convex clusters which are based on structural properties of the parallel applications. The developed algorithm is assessed by simulations run on some families of synthetic task graphs and randomly generated applications. The comparison with related approaches emphasizes its interest.