For humans, walking and adapting in various terrains and environments is a natural task. However, strange as it sounds, teaching a robot to take its first steps offers great challenges. Robot adaptation and control is an endless challenge that a substantial amount of researchers strive to solve. The evolutionary robotic research field aims to solve such problems using natural evolution as inspiration. This thesis explores a one-legged pneumatic jumping robot's ability to automatically adapt to the environment using an evolutionary algorithm. The purpose of this study is to discover whether the robot can automatically adapt to the real world environment by finding optimal gait and morphology. In order to discover whether a robot can adapt to the real world environment, a testing environment and two 3D printed robots were made. The first robot laid the groundwork for the second robot, and was designed to only optimize its gait, in which it successfully achieved using an evolutionary algorithm. The second robot was partly able to achieve its design purposes in terms of optimizing gait and morphology. Several experiments were carried out on the two robots. An early finding was that it is possible to use the evolutionary algorithm to optimize the first robot's gait. To make the second robot adapt to the natural environment, however, was a more complicated matter as it encountered various challenges leaving the investigation imperfect. On the other hand, the evolutionary algorithm itself performed quite well and the robots was ultimately fit to handle the major stress inflicted by the explosive pneumatic system.