Cryptography is a branch that plays a major role in modern computing - preser- ving security. The goal of cryptography is to prevent attackers from reading messages traversing a communication channel. This is achieved using special cryptographic al- gorithms that convert exposed data into encrypted, unreadable data. In this paper I will attempt to make a link between cryptography and evolutionary algorithms using genetic programming. Evolutionary algorithms are a subset of algo- rithms that use relationships found in nature in order to generate a solution to a given problem. In this context, the task was creating an encryption algorithm using a tree structure. The primitives that make up the tree are inspired by hitherto known encryp- tion algorithms as they determine the encryption steps. With the help of evolution and evolutionary operators, genetic programming with each iteration searches for more su- perior solutions. The search for an encryption algorithm was based on adversarial machine learning of the participants in the communication process. For those participants the goal is to keep the communication secret, ie. incomprehensible and impossible to decrypt for a participant without a key. On the other hand, an attacker is a participant who listens to that communication and aims to decrypt it. The thing that differs them from legitimate participants is that that the attacker doesn’t have a key. Therefore, we define functions of goodness for a participant’s solution so that in case an attacker simply decrypts the message, the participant who encrypted it has a diminished goodness and vice versa. It is also important to the sender who encrypts the data that the participant to whom the message is sent can decrypt it. Consequently, their goodnesses are in positive cor- relation. In the end, the results showed that there are some communication parameters that can be optimized to better the communication. Those are, primarily, the primitive sets used in constructing the algorithm and the text length.