The automatic design of well-performing robotic controllers is still an unsolved problem due to the inherently large parameter space and noisy, often hard-to-define performance metrics, especially when sequential tasks need to be accomplished. Distal control architectures, which combine precoded basic behaviors into a (probabilistic) finite state machine offer a promising solution to this problem. In this paper, we enhance a Mixed-Discrete Particle Swarm Optimization (MDPSO) algorithm with an Optimal Computing Budget Allocation (OCBA) scheme to automatically synthesize distal control architectures. We benchmark MDPSO-OCBA’s performance against the original MDPSO as well as the Iterated F-Race (IRACE) and the Mesh Adaptive Direct Search (MADS) algorithms on both a benchmark function with different noise levels and design problems of distal control architectures. More specifically, we evaluate the algorithms using high-fidelity simulations in three increasingly challenging scenarios involving parallel and sequential tasks. Additionally, the best performing controller generated in simulation by each optimization algorithm is compared with a manually designed solution and validated with physical experiments. The analysis on the benchmark function with different noise levels demonstrates MDPSO-OCBA’s high robustness to noise. The comparison on the robotic control design problems shows that, without any meta-parameter tuning, MDPSO-OCBA is able to generate the best performing control architectures overall, closely followed by IRACE. They significantly outperform MADS for the more complex and noisier scenarios, resulting in competitive controllers in comparison to the manually designed one.