Automatic lane change of intelligent vehicles is a complex process. Besides of safety, feelings of the driver and passengers during the lane change are also very important. In this paper, a lane change trajectory planner is designed to generate an ideal collision-free trajectory to satisfy the driver's preference. Various lane changing modes, gentle lane change, general lane change, radical lane change and personalized lane change, are designed to meet the needs of different passengers on vehicles simultaneously. In this paper, the condition of the two-lane change is studied. One vehicle is in front of the ego vehicle at the same lane and one is at the rear of the ego vehicle at the target lane. A trajectory planning method is then established based on constant speed offset and sine curve, vehicle distances and speed difference, etc. The key factors which can reflect drivers’ lane change characteristics are then acquired. Based on the key factors, lane change decision model and lane change state model are established, which can reflect drivers’ personalized lane change selection and habits based on the traffic environment. The effectiveness of lane change decision model is validated by computer simulations. In order to fit the lane change state model, a BP neural network controller is then developed. The small errors of predicted lane change time demonstrate the effectiveness of the BP neural network. Finally, lane changes with different modes are conducted in MATLAB under different vehicle distances and speed difference. Simulation results demonstrate that the proposed trajectory planner can generate collision-free trajectories and shows a good reflection of driver lane change styles. Additionally, multiple lane changing modes add the probability of practical applications. This paper can provide reference for lane change trajectory planning of intelligent vehicles.