There is no doubt that the smallest spacecraft platforms (CubeSats, NanoSats and microsats) are revolutionizing the market. The possibilities of developing spacecraft through standard platforms, commercial of the shelf components and then reduced the costs of development has allowed new organizations to enter the space section. On top of that, traditional businesses have been benefited by these type of new spacecraft, as more technology demonstrations and riskier missions have been able to be developed through this technology. Nevertheless, these spacecraft systems cannot compete with their larger counterparts due to their size limitations. The extra space will allow for larger spacecraft to accommodate more complex and precise instruments and payloads. Therefore, an individual CubeSat will never be able to compete with a larger system. Nevertheless, in the last two decades, the concept of using the combined power of these simple and cheap systems to perform a task only possible with larger spacecraft has been proposed. One of the most interesting possibilities among the use of these distributed space systems is the use of swarming. Swarming is a technique which consists of the combination of a large number of simple elements to allow for the emergence of properties that allow the whole system to act as more than just its parts. The benefits of spacecraft swarming are undeniable, as inherently swarming brings robustness, flexibility, and scalability to the system, all three properties extremely valued in space missions.\\ For the moment spacecraft swarming has been mostly based on generating formation patterns with a large degree of autonomy. Still, only a handful of techniques have been explored. This work aims to further advance the knowledge of spacecraft swarming techniques. To do so, the implementation of a swarming algorithm never tested in space will be studied The advantage of this algorithm is double. First, as all swarming techniques, it presents a certain degree of artificial intelligence that will allow the swarm to cope with unexpected situations. Second, by design, this algorithm requires really little knowledge of the swarm environment. This translates in less need for information processing between spacecraft and within each spacecraft. Since the application is focused on already spacecraft with limited capabilities, this is certainly advantageous. To fully understand the work here presented, it is recommended that the reader possesses graduate-level knowledge on orbital dynamics and control, as well as some undergraduate notions of computer programming and artificial intelligence techniques. If the reader lacks knowledge on any of the mentioned areas, it is recommended to read the associated literature study referenced in the bibliography.