In modern smart buildings, the electricity consumption of a building is monitored every time and costs differently at each time slot of a day. Smart buildings are also equipped with indoor sensors that can track the movement of human beings. In this paper, we propose a new elevator control system (ECS) that utilizes two kinds of context information in smart buildings: (1) human movements estimated by indoor sensors and (2) dynamic changes of electricity price. In particular, indoor sensors recognize elevator passengers before they press the elevator call buttons, and smart meters inform the dynamically changing price of the electricity to ECS. By using this information, our ECS aims at minimizing both the electricity cost and the waiting time of passengers. As this is a complex optimization problem, we use an evolutionary computation technique based on genetic algorithms (GA). We inject a learning module into the control unit of ECS, which monitors the change of the electricity price and the passengers' traffic detected by sensors. Experimental results with the simulator we developed show that our ECS outperforms the scheduling configuration that does not consider sensor information or electricity price changes.