This paper describes a genetic algorithm for predicting RNA structures that contain various types of pseudoknots. Pseudoknotted RNA structures are much more difficult to predict by computational methods than RNA secondary structures, as they are more complex and the analysis is time-consuming. We developed an efficient genetic algorithm to predict RNA folding structures containing any type of pseudoknot, as well as a novel initial population method to decrease computational complexity and increase the accuracy of the results. We also used an interaction filter to decrease the size of the possible stem lists for long RNA sequences. We predicted RNA structures using a number of different termination conditions and compared the validity of the results and the times required for the analyses. The algorithm proved able to predict efficiently RNA structures containing various types of pseudoknots.