<p>Ridesharing has emerged as a transformative mode of travel in the sharing economy, enabling passenger transportation without increasing car ownership. In a ridesharing system, drivers fulfill ride requests while transporting multiple passengers, navigating constraints such as time windows, capacity, passenger assignments, and demand satisfaction. To align with the principles of sustainable urbanism, ridesharing systems must be reexamined through economic, environmental, and social lenses. This paper addresses a sustainable ridesharing routing and scheduling problem by proposing a multi-objective optimization model that evaluates the system’s performance from these three perspectives. The model aims to minimize total cost, carbon emissions, and passenger delays. To solve the problem, we develop a novel algorithm, the Multi-Objective Adaptive Large Neighborhood Search (MOALNS), which incorporates tailored removal and insertion heuristics designed for this context. Comprehensive analyses demonstrate the superior performance of MOALNS across key multi-objective metrics, including Hypervolume, Spacing, and Inverted Generational Distance (IGD), compared to state-of-the-art algorithms. These findings advance the field of sustainable transportation and offer practical insights for implementing efficient and sustainable ridesharing systems.</p>