© 2017 The Authors. Published by Eccomas Proceedia. History Matching (HM) is a form of model calibration suitable for high-dimensional and computationally expensive numerical models. It sequentially cuts down the input space to find the non-implausible domain that provides a reasonable match between the output and experimental data. The non-implausible domain can be orders of magnitude smaller than the original input space and it can have a complex topology. This leads to one of the most challenging open problems in implementing HM, namely, the efficient generation of samples in the non-implausible set. Previous work has shown that Subset Simulation can be used to solve this problem. Unlike Direct Monte Carlo, Subset Simulation progressively decomposes a rare event (here is the non-implausible set), which has very small failure probabilities, into sequential less rare nested events. The original Subset Simulation uses a Modified Metropolis algorithm to generate the conditional samples that belong to intermediate less rare failure events. Generating samples moving forwards to the target space is the heart for Subset Simulation. This work considers different sampling strategies to generate samples and compares their performance in the context of expensive model calibration. A numerical example is provided to show the potential of HM using different Subset Simulation sampling schemes.