Understanding the physics of small bodies such as asteroids, comets, and planetary moons will help us understand the formation of the solar system, and also provide us with resources for a future space economy. Due to these reasons, missions to small bodies are actively being pursued. However, the surfaces of small bodies contain unpredictable and interesting features such as craters, dust, and granular matter, which need to be observed carefully before a lander mission is even considered. This presents the need for a surveillance spacecraft to observe the surface of small bodies where these features exist. While traditionally, the small body exploration has been performed by a large monolithic spacecraft, a group of small, low-cost spacecraft can enhance the observational value of the mission. Such a spacecraft swarm has the advantage of providing longer observation time and is also tolerant to single point failures. In order to optimize a spacecraft swarm mission design, we proposed the Integrated Design Engineering & Automation of Swarms (IDEAS) software which will serve as an end-to-end tool for theoretical swarm mission design. The current work will focus on developing the Automated Swarm Designer module of the IDEAS software by extending its capabilities for exploring surface features on small bodies while focusing on the attitude behaviors of the spacecraft in the swarm. We begin by classifying spacecraft swarms into 5 classes based on the level of coordination. In the current work, we design Class 2 swarms, whose spacecraft operate in a decentralized fashion but coordinate for communication. We demonstrate the Class 2 swarm in 2 different configurations, based on the roles of the participating spacecraft. The attitude behaviors of all the spacecraft are then converted to a line of sight (LoS) tracking problem with respect to different targets depending on their role in the swarm. A sliding mode control law is used to track the LoS with respect to assigned targets. Following this, we formulate the surface feature problem as an optimization problem which is solved using genetic algorithm optimization. Finally, the principles described are demonstrated by a numerical simulation of observing a simulated surface feature over the surface of asteroid 433 Eros. The results indicate successful performance of the design and control algorithms.