Casing design plays an important role in the successful drilling of a well and accounts for a substantial percentage of well costs. The goal of casing design is to get an optimal design that will withstand stress and other factors that affect casing throughout the lifetime of the well. The conventional approach to casing design uses a deterministic working stress design (WSD), where minimum strength requirements of the casing are determined by comparing casing strength to the magnitude of severe accidental loads that may occur during the lifetime of the well. Uncertainties in the load and strength of the casing are accounted for by multipliers called safety factors (SFs) that are mostly based on experience and do not reflect the probability or consequence of the different casing failure modes. This approach may result in overly conservative casing designs, or design requirements for severe conditions that are expensive, leading to higher well costs. In this paper, the Monte Carlo Simulation (MCS) method is applied to casing design, where uncertainties in casing loads are considered explicitly by assigning probability distributions to safety factors that affect design loads. As the MCS method predicts the casing safety factor probability, it gives a better view of the real uncertainties involved in the design. Acceptable probabilities of casing design load can be selected based on the cost and operating conditions the casing will undergo; thus, a probability approach to casing design is more flexible as it allows an explicit uncertainty-consistent designs compared to traditional working stress designs.