Privacy and utility are two important objectives to consider when releasing census data. However, these two objectives are often conflicting, as protecting privacy usually necessitates introducing noise into the data, which compromises data utility. Determining the appropriate level of privacy protection presents a significant challenge in the data release. Therefore, it is necessary to investigate the tradeoff between privacy and utility before making a final decision on the level of privacy protection. In this article, we propose a multiobjective optimization framework to generate multiple optimal solutions that satisfy the two objectives of privacy and utility, as well as to analyze the tradeoff between privacy and utility for decision-making. This framework relocates individuals susceptible to revealing their identities to protect their privacy. We maximize the number of individuals relocated while maximizing the utility of the data after relocations. The proposed framework is tested using synthetic population data in Franklin County, Ohio. Our experimental results show that the framework can efficiently generate a collection of optimal solutions and can be used to effectively balance privacy and utility.