The ongoing development in additive manufacturing opens up new possibilities for the creation of electronics with distinctive design freedom. The present work focuses on optimizing the high-frequency properties of additively manufactured electronic components by examining the effects of various manufacturing strategies on the microstructure and the resulting high-frequency properties. As this is a highly dynamic problem on the microsecond and micrometer scale, computational fluid dynamics simulations was used to develop a block-based printing strategy. The results reveal that the new printing strategy significantly improves high-frequency properties through a smoother microstructure. Applying machine learning to the simulation results provides deep insights into the high-dimensional parameter space, which can be reduced to a fraction of the computing time using active learning methods.