The unmixing method for detrital zircon U-Pb age distribution based on simulation statistics has issues with high computational complexity, inconsistent evaluation criteria, and poor algorithm convergence. This paper proposes a novel multi-objective optimization method for the unmixing of detrital zircon U-Pb age distribution. It comprehensively considers multiple quantitative similarity metrics for U-Pb age distribution and designs a multi-objective optimization unmixing model. An improved NSGA-II-based algorithm is implemented to achieve the unmixing of detrital zircon U-Pb age distribution. Compared to the commonly used Inverse Monte Carlo method, this approach significantly reduces computational complexity and efficiently achieves sampling and mixing simulations of complex source-to-sink systems, yielding better provenance analysis results. Model tests and practical applications have demonstrated the effectiveness and significant application potential of the multi-objective optimization method for detrital zircon U-Pb age distribution unmixing.