Software can be optimised in various ways, e.g., by changing the code directly, modifying compiler or software’s paramters. To automate these tasks, algorithm configuration and genetic improvement have been proposed where one modifies parameters and the other source code. Several tools have been introduced to facilitate such changes automatically. However, these tools only work at a single code level, either optimising a parameter or modifying source code. In 2022, Blot and Petke introduced MAGPIE, which is a framework that is capable of simultaneously searching for improvement at different granu- larity levels. From our literature review, we found that the best search strategies in genetic improvement and algorithm configuration, that generalise to both domains, are based on local search and genetic algorithms, respectively. We thus compared the two approaches for runtime improvement of the MiniSAT solver. We also explored the two search strategies on the joint search space of parameter and source code edits. We found that genetic improvement with first improvement local search led to the best results by improving MiniSAT’s runtime by 18.05%.