The gig economy is considered the future of work models by many scholars across the globe. But the gig economy comes with a set of unique challenges like scheduling, performance measurement, and worker satisfaction. This paper explores optimizations within the gig economy, aimed at suggesting solutions for the above challenges across platforms like ride sharing, freelancing, and food delivery. Efficient workforce management is especially difficult in the gig economy, owing to its non-traditional structure comprising fluctuating worker availability and variable demand. To address these characteristics, we develop models aimed at optimizing the workforce in real-time based on the real-time availability of workers and predicted demand. Our performance measurement methodology adjusts to the varied nature of gig jobs by introducing flexible, task-specific measures to assess the dependability, efficiency, and quality of services provided by gig workers. To capture the elements most important to gig workers’ well-being, we also suggest an employee satisfaction model that considers incentive structures, flexibility, and income stability. The suggested models are evaluated in a range of supply and demand scenarios using scenario-based analysis and simulation, showing increases in worker satisfaction, task completion, and service coverage. This study provides gig platforms with a methodical way to improve worker satisfaction and operational effectiveness.