A big proportion of hospital-associated infections caused by Staphylococcus aureus can be attributed to Methicillin-resistant S. aureus (MRSA). Many countries take interventions to try and minimise the spread of MRSA. Interventions, such as a search-and-destroy policy and restrictive antibiotics use, have proven to be effective. Different strains of MRSA are grouped into clonal complexes (CCs) by their similarity to a central allelic profile. Separate MRSA CCs have evolved independently over time. In most countries a limited number of CCs is responsible for the prevalence in the population. In each country different CCs are present and the exact reasons why these CCs are successful in the specific countries is unknown. The aim of this project was therefore to study two different models, one implemented in R and one implemented in Java, that both simulate the spread of multiple MRSA CCs in a population. The two individual based models (IBMs) considered in this research produce similar results when setting them side-by-side using simple model set-ups. The R model turned out to be computationally expensive and very restrictive, where on the other hand, the Java model was much faster and more extensive. Consequently, the Java model was used to simulate the spread of MRSA CCs in a more advanced setting. Although the model set-up and population parameters were not yet realistic, it demonstrated some interesting findings. The general observation was that CCs with higher antibiotic resistance contribute most of the MRSA infections. The model showed that intervention by means of a search-and-destroy policy can lower the overall prevalence in a population significantly and create more variation between the CCs present. Since the model set-ups adopted during this research most likely do not completely agree with the biological processes, populations and interventions in the real world, future research should determine whether the obtained exploratory results also hold true in more representative populations.