Distributed systems, such as blockchains, can have bugs around edge-cases that are hard to detect or trigger. Previous publications have introduced guided-search testing approaches that are able to find edge cases more efficiently than through conducting a systematic and exhaustive search. In this paper, we compare the effectiveness of fitness functions in evolutionary testing frameworks. For this we evaluate time and proposal fitness. While evolutionary testing frameworks are not new to the domain of concurrency testing consensus algorithms, the impact of the fitness functions that underpin them remains poorly understood. We use the XRPL consensus algorithm as a case study to evaluate the fitness functions using the Rocket testing framework. For this, we make use of seeded versions of XRPL. All evaluated fitness functions have been able to detect the bugs we seeded in the source code of the XRPL consensus algorithm. We show the validity of various fitness functions in trying to find the bug and analyze effects in the interplay between the time and proposal fitness functions we examine.