The objective of my Ph.D. research is to test the hypothesis that large-scale discrete multidisciplinary design optimization (MDO) can maximize complex, next-generation engineering systems’ performance automatically, which has not been possible with existing numerical methods. Specifically, I am looking at a new optimization framework considering low-thrust trajectory optimization with discrete fly-by options to enable more frequent and affordable missions, which aligns well with NASA’s mission to explore and extend our knowledge about the universe. MDO is a promising approach to tackle the above optimization because it can automatically use multiphysics simulations to find the best possible design, significantly reducing the design time. Existing gradient-based MDO algorithms can efficiently handle many design variables but cannot deal with discrete variables. In my PhD research, I will create a new large-scale discrete MDO framework (LSDMDO) to tackle the above challenge. LSDMDO is a novel class of optimization algorithms that efficiently synergize the gradient-based and evolutionary optimization methods to enable large-scale MDO problems with discrete variables. The LSDMDO algorithm will be rigorously derived, characterized, and evaluated in my PhD research.