Type II diabetes mellitus is a disorder that disrupts the way the body uses glucose. It also causes other problems with the way the body stores and processes other forms of energy, including fat. It is managed by close monitoring of blood glucose level while the clinician experiments with dosing strategy based on some clinical guidelines and his/her own experience. In this study, we propose a treatment planning model that optimizes the dosing strategy for diabetes treatment. The model utilizes patient’s personalized characteristics of disease progression and dose response to optimize the drug dosage. Such personalized evidence is estimated by a novel drug-dose-drug-effect predictive model using the daily blood glucose data recorded during the titration period. We apply these to de-identified data from a group of patients suffering from gestational diabetes. For each patient, drug-dose-drug-effect prediction was established based on the first three weeks of self-monitored blood glucose. The treatment model then individualizes and optimizes dose regimen based on the patient’s personalized dose-effect characteristics. Consistently, the optimized dose regimens use less amount of drug while achieving better glycemic control than the original regimens used to treat the patients. This results in the first mathematical model that is data-driven evidence-based that quantitatively optimizes dosage for the treatment of diabetes. The model can generate personalized dose regimen that has better treatment outcome and more drug efficient. Clinical trials must be conducted to gauge the overall effectiveness.