By exploiting the generalized form of Snell’s law, metasurfaces afford optical engineers a tremendous increase in degrees of design freedom compared to conventional optical components. These “meta-optics” can achieve unprecedented levels of performance through engineered wavelength-, angular-, and polarization-dependent responses which can be tailored by arranging subwavelength unit cells, or meta-atoms, in an intelligent way. Moreover, these meta-atoms can be constructed from phase change materials which give the added flexibility of realizing reconfigurable meta-optics. Devices such as achromatic flat lenses and non-mechanical zoom lenses are becoming a reality through the advent of metasurface-augmented optical systems. However, achieving high-performance meta-optics relies heavily on proper meta-atom design. This challenge is best overcome through the use of advanced inverse-design tools and state-of-the-art optimization algorithms. To this end, a number of successful meta-device inverse-design approaches have been demonstrated in the literature including those based on topology optimization, deep learning, and global optimization. While each has its pros and cons, multi-objective optimization strategies have proven quite successful do their ability to optimize problems with multiple competing objectives: a common occurrence in optical design. Moreover, multi-objective algorithms produce a Pareto Set of optimal solutions that designers can analyze in order to directly study the tradeoffs between the various design goals. In our presentation, we will introduce an efficient multi-objective optimization enabled design framework for the generation of broadband and multifunctional meta-atoms. Additionally, several meta-optic design examples will be presented, and future research directions discussed.