Public health researchers use serological studies to obtain serum samples from individuals and measure antibody levels against one or more pathogens. When paired with appropriate analytical methods, this data can be used to determine whether individuals have been previously infected with or vaccinated against those pathogens. However, there is currently a lack of tools to simulate realistic serological study data from the processes determining these observed antibody levels. We developed serosim, an open source R package which enables users to simulate serological study data matching their disease system(s) of interest. This package allows users to specify and modify model inputs responsible for generating an individual’s antibody measurements at various levels, from the within-host processes to the observation process. serosim will be useful for designing more informative serological studies, better understanding the processes behind observed serological data, and assessing new serological analytical methods.