OBJECTIVES: Knowledge of Maturity Onset Diabetes of the Young (MODY) presence and the specific genetic mutation that causes it can lead to targeted treatment and improved health outcomes for patients with diabetes. Yet, testing all diabetic patients for the presence of MODY is not cost-effective as MODY prevalence is low and MODY test cost is high. We developed a stratification algorithm that allows us to target the MODY test at a subgroup of patients most likely to have the mutation. This study examines the incremental cost-effectiveness of a MODY test driven by this algorithm versus no genetic testing. METHODS: A decision tree was constructed to estimate the costs and effectiveness of the algorithm-driven MODY testing and no genetic testing strategies over a 30 year time horizon from a payer’s perspective. Singapore-specific costs and prevalence of MODY obtained from local studies and utility values sourced from the literature are used to populate the model.  RESULTS: The algorithm-driven MODY testing strategy costs more than the no genetic testing strategy (i.e. US$9,888 vs. US$9,426) but generates slightly more Quality Adjusted Life Years (QALYs) (i.e. 13.96 vs 13.95), yielding an incremental cost effectiveness ratio (ICER) of US$66,111/QALY. Sensitivity analyses indicate that the algorithm-driven MODY testing strategy would be cost-effective against the commonly used cost effectiveness threshold of US$50,000/QALY if the prevalence of MODY mutations increases or if the cost of the MODY test drops from US$1,050 to below US$778.  CONCLUSIONS: Algorithm-driven testing for MODY is close to but not yet cost effective based on the cost effectiveness threshold of US$50,000/QALY. As genetic testing prices continue to fall, the algorithm-driven MODY testing strategy is likely to be cost-effective in the near future.