Objective <p>To develop a patient-level simulation model of type 1 diabetes (T1D) covering both childhood and adulthood. The goal is to identify and evaluate the cost-effectiveness of optimal screening for pre-symptomatic T1D.</p> Methods <p>We developed a Python-based simulation model to track 100,000 participants screened in childhood, capturing a subset of those at risk and transitioning to T1D, to estimate the incremental cost-effectiveness per life year gained of screening versus no screening. Our multi-objective optimisation approach sought to minimise three objectives: incremental cost effectiveness ratio, diabetic ketoacidosis (DKA) events at onset and the maximum number of screening tests a child can have with the healthcare system. The NSGA-II algorithm is used to explore the set of possible screening strategies from combinations of genetic risk score (GRS) and islet autoantibody (IA) measurements at different ages and frequencies during the first 15 years of life. Data for transition probabilities include large scale screening studies such as The Environmental Determinants of Diabetes in the Young, TrialNet, published risk functions, clinical trials and epidemiologic studies.</p> Results <p>We illustrate the use of multi-objective optimisation in patient-level simulations by estimating an optimal subset of T1D screening strategies in the USA. We identify four screening strategies with incremental cost-effectiveness ratios that meet commonly cited cost-effectiveness thresholds, which require, respectively, a maximum of 1, 2 3 and 4 islet autoantibody (IA) tests.</p> Conclusions <p>This article and corresponding model code can be used as a reference for implementing a multi-objective optimisation pipeline in patient-level simulation models</p>