In this paper, we propose a stochastic model for hardware software cosynthesis of a heterogeneous, multiprocessor computing system dedicated for a specified real time application. In this model, the task execution times and the data transfer times are taken to be random variables. Based on the stochastic framework, we derive a method for generating optimum task schedules and evaluating the performance of the architecture. The pool of resources required for building the architecture and the task allocations are optimized by a genetic algorithm. We demonstrate that this approach produces architectures which are superior in terms of cost and processor utilization. Moreover, it yields good solutions even in situations where no feasible solution could be produced using deterministic timings. The scheduling algorithm has a polynomial time complexity. The components of the architecture are evolved in a hierarchical manner, progressively refining it by applying the genetic algorithm in distinct phases. This provides a powerful CAD tool for cosynthesis which can generate a range of optimum solutions with exchangeable cost and performance benefits.