Cyber-Physical Systems (CPSs) have become an intrinsic part of the 21st century world. Systems like Smart Grids, Transportation, and Healthcare help us run our lives and businesses smoothly, successfully and safely. Since malfunctions in these CPSs can have serious, expensive, sometimes fatal consequences, System-Level Formal Verification (SLFV) tools are vital to minimise the likelihood of errors occurring during the development process and beyond. Their applicability is supported by the increasingly widespread use of Model Based Design (MBD) tools. MBD enables the simulation of CPS models in order to check for their correct behaviour from the very initial design phase. The disadvantage is that SLFV for complex CPSs is an extremely time-consuming process, which typically requires several months of simulation. Current SLFV tools are aimed at accelerating the verification process with multiple simulators that work simultaneously. To this end, they compute all the scenarios in advance in such a way as to split and simulate them in parallel. Furthermore, they compute optimised simulation campaigns in order to simulate common prefixes of these scenarios only once, thus avoiding redundant simulation. Nevertheless, there are still limitations that prevent a more widespread adoption of SLFV tools. Firstly, current tools cannot optimise simulation campaigns from existing datasets with collected scenarios. Secondly, there are currently no methods to predict the time required to complete the SLFV process. This lack of ability to predict the length of the process makes scheduling verification activities highly problematic. In this thesis, we present how we are able to overcome these limitations with the use of a data-intensive simulation campaign optimiser and an accurate machine-independent execution time estimator.