The evaluation of the best system-level architecture in terms of energy and performance is of mainly importance for a broad range of embedded SOC platforms. In this paper, we address the problem of the efficient exploration of the architectural design space for parameterized microprocessor-based systems. The architectural design space is multi-objective, so our aim is to find all the Pareto-optimal configurations representing the best power-performance design trade-offs by varying the architectural parameters of the target system. In particular, the paper presents a Design Space Exploration (DSE) framework tuned to efficiently derive Pareto-optimal curves. The main characteristics of the proposed framework consist of its flexibility and modularity, mainly in terms of target architecture, related system-level executable models, exploration algorithms and system-level metrics. The analysis of the proposed framework has been carried out for a parameterized superscalar architecture executing a selected set of benchmarks. The reported results have shown a reduction of the simulation time of up to three orders of magnitude with respect to the full search strategy, while maintaining a good level of accuracy (under 4% on average).