In order to realize lateral riding control, active control parameters of secondary suspension were optimized by utilizing characteristics of locomotive running in the same line interval of iterative learning method. A PD type closedloop iterative learning controller is selected for a highspeed locomotive to realize adaptive adjustment of active control parameters of vehicle body lateral stability under different wheelrail contact conditions, through Multiobjective optimization programming method. Based on pseudoexcitation method, the frequency domain riding index of the locomotive linear model with secondary lateral suspension active control is calculated. Compared with time domain simulation calculation, this method has advantages in computing speed. The results show that the carbody lateral riding index converges quickly after fifth iterations with optimized control parameters．The method can automatically adapt to the locomotive wheelrail contact state and line state, and its lateral performance is improved significantly than the ILC method with fixed control parameters. In addition, the method still has good control effect in a certain time delay range when considering time delay of the control system. Finally, the paper pointed out that the system time delay should be controlled within 100ms to prevent rapid deterioration of the train's lateral dynamic performance．