In this paper, we address the problem of the efficient exploration of the architectural design space for parameterized systems. Since the design space is multi-objective, our aim is to find all the Pareto-optimal configurations that represent the best design trade-offs by varying the architectural parameters of the target system. In particular, the paper proposes a Design Space Exploration (DSE) framework based on a random search algorithm that has been tuned to efficiently derive Pareto-optimal curves. The reported design space exploration results have shown a reduction of the simulation time of up to two orders of magnitude with respect to full search strategy, while maintaining an average accuracy within 3%.