This paper presents a method to incorporate personalized visual preferences in real-time optimal daylighting control without using general discomfort-based assumptions. A personalized shading control framework is developed to maximize occupant satisfaction while minimizing lighting energy use in daylit offices with automated shading systems. Personalized visual satisfaction utility functions were used along with model-predicted lighting energy use to form an optimization framework using two approaches. In the multi-objective optimization scheme, the satisfaction utility and predicted lighting energy consumption are used as parallel objectives to provide a set of Pareto solutions at each time step. In the single-objective optimization scheme, the satisfaction utility is converted into a constraint when minimizing lighting energy use. A simulation study with two distinct visual satisfaction models, inferred from experimental data, was conducted to evaluate the implementation feasibility and optimization effectiveness. Daily and annual simulation results are presented to demonstrate the different patterns of optimal points depending on preference profiles, occupant sensitivity to utility function, and exterior conditions. Finally, we present a new way to apply the multi-objective optimization without assigning arbitrary weights to objectives: allowing occupants to be the final decision makers in real-time balancing between their personalized visual satisfaction and energy use considerations, within dynamic hidden optimal bounds. A slider is introduced as a dynamic user interface with mapped and sorted optimal solutions.