Micro-transit services offer a promising solution to enhance urban mobility and access, particularly by complementing existing public transit. However, effectively designing these services requires determining optimal service zones for these on-demand shuttles, a complex challenge often constrained by operating budgets and transit agency priorities. This paper presents a novel two-phase algorithmic framework for designing optimal micro-transit service zones based on the objective of maximizing served demand. A key innovation is our adaptation of the shareability graph concept from its traditional use in dynamic trip assignment to the distinct challenge of static spatial zoning. We redefine shareability by considering geographical proximity within a specified diameter constraint, rather than trip characteristics. In Phase 1, the framework employs a highly scalable algorithm to generate a comprehensive set of candidate zones. In Phase 2, it formulates the selection of a specified number of zones as a Weighted Maximum Coverage Problem, which can be efficiently solved by an integer programming solver. Evaluations on real-world data from Chattanooga, TN, and synthetic datasets show that our framework outperforms a baseline algorithm, serving 27.03% more demand in practice and up to 49.5% more demand in synthetic settings.