AML is a cancer of the blood and bone marrow which results from the combined effects of genetic mutations, aberrant interactions in the microenvironment and altered networks of complex chemical reactions at the molecular and cellular level, some of which can be targeted with anti-neoplastic drugs called chemotherapy. AML can be treated with chemotherapy, the types and doses of which are dependent on characteristics of the patient, the sub-type of the tumor and the use of other, often synergistic, anti-cancer drugs, and the doses for which are limited by toxic adverse effects of treatment. Current treatment protocols are designed based on pre-clinical animal experiments and on empirical clinical trials as well as the acquired experience of subspecialist physicians. Mathematical modeling can assist in improving chemotherapy effectiveness and limiting toxicity through a systematic approach in designing treatment protocols. Specifically, these mathematical models should enable a description of the normal and the leukemic cell populations as dependent on disease characteristics (cell cycle distribution into phases, proliferation rate, initial disease and normal population state) and on physiological characteristics of the patient such as age, sex, body surface area that control and define the drug kinetics (concentration profile in tumor site). Such a model can then lead to an optimal management of the available drug kinetics in order to effectively eradicate the maximum possible tumor volume while limiting toxicity of the normal cell population and that will be maintained within certain defined limits.Herein, a model is presented for the first cycle of chemotherapy induction treatment for AML using daunorubicin (DNR) and cytarabine (Ara-C) anti-leukemic agents, a standard intensive treatment protocol for AML. The proposed model combines critical targets of drug actions on the cell cycle, together with pharmacokinetic (PK) and pharmacodynamic (PD) aspects providing a complete description of drug diffusion and action after administration. Tumor-specific characteristics, such as tumor burden and cell cycle times, as well as patient-specific characteristics, such as gender, age, weight and height, are incorporated into the model in an attempt to gain insights into the personalized cell dynamics during treatment. Moreover, an optimal control problem is formulated and solved so as to obtain the chemotherapeutic schedule which would maximize leukemic cell kill (therapeutic efficacy) while minimizing death of the normal cell population, thereby reducing toxicities. Simulation results for a standard treatment protocol are obtained for a patient case study; an optimized treatment schedule is also obtained and the cell populations are analyzed and compared in detail for both the standard and the optimized treatment protocols.