In the context of the design on time-critical systems, analytical models with worst case workloads are used to identify safe solutions that guarantee hard timing constraints. However, the focus on the worst case often leads to unnecessarily pessimistic and inefficient solutions, in particular for mixed-critical systems. To overcome the situation, the paper proposes a novel design flow integrating analytical and simulation-based Design Space Exploration (DSE). This combined approach is capable to find more efficient design solutions, without sacrificing timing guarantees. For it, a first analytical DSE phase obtains a set of solutions compliant with the critical time constraints. Search of the Pareto optimum solutions is done among this set, but it is delegated to a second simulation-based search. The simulation-based search enables more accurate estimations, and the consideration of a specific (or an average-case) scenario. The chapter shows that this can lead to different Pareto sets which reflect improved design decisions with respect to a pure analytical DSE approach, and which are found faster than through a pure simulation-based DSE approach. This is illustrated through an accompanying example and a proof-of-concept implementation of the proposed DSE flow.