Cooperative planning of Connected and Autonomous vehicles (CAVs) is a promising way to reshape the intelligent transportation system, and planning under scenarios mixed with human-driven vehicles is one of the critical challenges. Many existing studies have proposed vehicle group planning methods towards mixed traffic scenes. By regarding the reward of all CAVs as a unified entity, the overall average driving performance was improved. However, one limitation is that during the collective planning process, individual demands are sacrificed for lack of considering agent personalized preferences. To balance the group common benefit and individual diversity, this paper proposes a novel cooperative multi-vehicle planning method combining collective decision-making and individual preference evaluation. First, a bi-level collective planning framework is designed including region-driven behavior selection and conflict-free trajectory generation. To further consider personalized features, an individual preference evaluation system is established based on Social Value Orientation. Then the evaluation results are merged into the group planning process so that vehicles can generate various decisions according to personal demands. Experimental results show that the driving personalization levels of safety, time efficiency, and comfort are increased by 1.28%, 3.78%, and 8.80%, correspondingly.