This work focuses on the Area Coverage Planning Problem (ACPP) for optical cameras onboard agile spacecraft in space exploration missions. The objective is to determine the optimal observation path of the camera’s boresight to obtain a mosaic that fully covers a designated Region Of Interest (ROI) on the target’s surface, while considering activity makespan and computational demand. To tackle this problem, four improved heuristics are implemented, each addressing differently the need to create an acquisition plan that dynamically adjusts to the camera’s observation geometry over time. Based on the proposal from a previous study, these heuristics have been further refined to correct spatial distortion and improve computational efficiency. In consequence, the current implementation allows for application to non-convex, irregularly shaped celestial bodies. We have developed a comprehensive program framework with supporting functions and assets to enable the iterative execution of the applied heuristics under diverse observation geometries along the spacecraft’s trajectory, ensuring their robustness and adaptability. The ACPP is a component of a broader scheduling problem for agile spacecraft, which aims to maximize the scientific return while adhering to geometric and operational constraints imposed by both the spacecraft and its payload. In this context, deterministic step-stare algorithms are preferred for their efficiency in balancing accuracy and computational resources. The algorithms are showcased through the simulation of observations from Galileo during one of its flybies over Europa. Arbitrary and diverse ROIs are considered on the target’s surface, allowing for a comprehensive evaluation of the algorithms in different observation geometries. The outcomes are analyzed over coverage completeness, efficient planning and computational burden. Thus, the resulting mosaics provide insights into the optimal usage of the heuristics in specific circumstances.