The use of automated optimisation in engineering applications is emerging. In particular, nature inspired algorithms are frequently used because of their variability and robust application in constraints and multi-objective optimisation problems. The purpose of this paper is the comparison of four different algorithms and several optimisation strategies on a set of seven test propellers in realistic industrial design setting. The propellers are picked from real commercial projects and the manual final designs were delivered to customers. The different approaches are evaluated and final results of the automated optimisation toolbox are compared with designs generated in a manual design process. We identify a two-stage optimisation for marine propellers, where the geometry is first modified by parametrised geometry distribution curves to gather knowledge of the test case. Here we vary the optimisation strategy in terms of applied algorithms, constraints and objectives. A second supporting optimisation aims to improve the design by locally changing the geometry, based on the results of the first optimisation. The optimisation algorithms and strategies yield propeller designs that are comparable to the manually designed propeller blade geometries, thus being suitable as robust and advanced design support tools. The supporting optimisation, with local modification of the blade geometry and the proposed cavity shape constraints, features particular good performance in modifying cavitation on the blade and is, with the AS NSGA-II (adaptive surrogate-assisted NSGA-II), superior in lead time.