In urban rail transit systems, energy conservation is a challenging problem due to the rising environmental and social issues. The existing literature on this topic usually ignores time-variant characteristics of passenger demand at each station. Based on the real-world time-dependent smart-card automated fare collection data, this paper develops a bi-objective timetable optimization model to minimize the total passenger waiting time and the pure energy consumption. In the model formulation, the total passenger waiting time is subjected to the train capacity in the oversaturated condition, and the pure energy consumption is represented by the difference between the traction energy consumption and the regenerative energy within a given period. Numerical examples based on the real-world data from Beijing Yizhuang metro line are conducted. The results indicate that the developed model can improve passenger service and reduce energy consumption efficiently in comparisons with the timetable used currently.