Robots have long been proposed as a solution for jobs that are difficult for humans, but their construction from nonrenewable and pollutant-causing materials presents a problem. The field of bio-robotics was developed, in part, to address this issue. In previous bio-robotic systems, such as Xenobots, AI-generated morphologies have been used to engineer desired behaviors in individual robots. However, this approach cannot be applied to biobots that are mass-fabricated as this limits our ability to control the behaviors of individual bots. While mass fabrication could have significant implications in the development of scalable biobot technologies for use in real-world applications, developing a reliable method to control their behavior remains a significant challenge. In this paper, we use evolutionary algorithms to create biobot swarm compositions that explore environments with varying obstacles efficiently at several scales. We demonstrate here that, while we cannot control the behavior of individual biobots, carefully selected swarm compositions can lead to desired behavior outcomes. This work thus provides one potential option for realizing biotechnology at scale, where mass-produced biobots must be filtered and combined appropriately.