Atopic dermatitis (AD) is a chronic inflammatory skin disease, affecting up to 25% of children worldwide. While the current mainstay of AD treatment is topical application of corticosteroids and emollients, clear guidance for effective use of these treatments, such as the frequency, duration and potency, are not fully established. This paper proposes a computational method to design personalized treatment strategies for AD by applying model predictive control (MPC) to a hybrid nonlinear mathematical model of AD pathogenesis. We demonstrate that our method can suggest effective schedules for corticosteroid treatments that can achieve long-term management of moderate to severe AD symptoms. The proposed MPC uses a single linearized model of AD for prediction of disease progression based on daily measurement of the disease state, and provides a computational framework to suggest personalized treatment strategies with a small computational burden.