Genetic Algorithms have been used as an Artificial Intelligence strategy for many games, including competitive, collectible card games. Hearthstone is a particularly popular online competitive, collectible card game, and poses many interesting Artificial Intelligence challenges such as deckbuilding and game-playing agents. While Genetic Algorithms have been used previously to build competitive Hearthstone decks, the many parameters that are required to be set for a Genetic Algorithm have not been explored in-depth. This project aims to explore and analyse the effects of altering parameters of a Genetic Algorithm for deckbuilding within Hearthstone, indicating interesting differences in deck performance. The analysis of the results indicates whether changing the mutation functions and fitness functions leads to a Genetic Algorithm evolving better Hearthstone decks. Moreover, the frequency of certain cards is analysed to identify whether certain playstyles are favoured by specific fitness functions, and whether there is a significant difference between the cards frequented by the decks evolved using each fitness function. It has been found that some fitness functions produce better decks than another with respect to the overall objective of Hearthstone: winning games. Moreover, the playstyles of frequented cards are found to differ between fitness functions.