Many legacy financial applications exist as a collection of formulas implemented in spreadsheets. Migration of these spreadsheets to a full-fledged system, written in a language such as Java, is an error- prone process. While small differences in the outputs of numerical calculations from the two systems are inevitable and tolerable, large discrepancies can have serious financial implications. Such discrepancies are likely due to faults in the migrated implementation, and are referred to as deviation failures. In this paper, we present a search-based technique that seeks to reveal deviation failures automatically. We evaluate different variants of this approach on two financial applications involving 40 formulas. These applications were produced by SEB Life & Pension Holding AB, who migrated their Microsoft Excel spreadsheets to a Java application. While traditional random and branch coverage-based test generation techniques were only able to detect approximately 25% and 32% of known faults in the migrated code respectively, our search-based approach detected up to 70% of faults with the same test generation budget. Without restriction of the search budget, up to 90% of known deviation failures were detected. In addition, three previously unknown faults were detected by this method that were confirmed by SEB experts.