OBJECTIVES: Pharmacological strategies for schizophrenia have received increasing attention due to the development of new and costly drug therapies. To estimate the direct healthcare and non-healthcare cost of schizophrenia and to simulate cost reductions potentially obtained with a new pharmacogenomics treatment, in a cohort of patients newly diagnosed with schizophrenia, over the first 5 years following their diagnosis. METHODS: A microsimulation Monte-Carlo Markov model was used. Six discrete disorder states defined the Markov model: 1): first episode (FE); 2) low dependency state (LDS); 3) high dependency state (HDS); 4) Stable state (Stable); 5) Well state (Well); and 6) Death state (Death). Costs and individual probabilities of transition were estimated from the Régie de l’assurance maladie du Québec and Med-Echo databases.   RESULTS: A total of 14,320 individuals were identified in the study cohort as newly diagnosed patients with schizophrenia. Over the first 5 years following diagnosis the mean cost per person was estimated at $36,701 (95%CI: 36,264 to 37,138). The direct health care cost accounted for 56.2% of the total cost, welfare assistance for 34.6% and long term care facilities for 9.2%. On the direct health care cost, hospitalisation cost accounted for 64.6%, medical cost for 11.4% and drug-related cost for 24%. In the case where a new pharmacogenomic treatment with 20% increase of effectiveness will be available, the direct healthcare and non-health care costs can be reduced up to 14.2%. CONCLUSIONS: This model is the first Canadian model incorporating transition probabilities adjusted for individual risk-factor profiles and costs using real-life data. Our results indicate that a new pharmacogenomics treatment could possibly reduce hospitalization and long-term care facility costs while potentially enabling patients to return to active employment that would in turn contribute to the reduction of the welfare assistance cost.