Based on a setup combining particle synthesis in continuous flow with online DLS detection and full computer automation, we have demonstrated that the problem of predictability of the outcome of an experiment can be solved by applying Bayesian optimization procedures. Reproducibility is improved by the flow synthesis procedure itself. For a series of polymers, we have shown that nanoparticle sizes can be obtained on demand by continuous optimization and iterative expansion of the training data set. While the number of iterations to reach a first target is about 20, typically any following selected target is reached with high efficiency, due to improved training sets. Overall, this provides a highly versatile instrument to tune parameters for a synthesis. More specifically, it allows to preselect any desired size almost independent from the type of BCP used (obviously only targets can be reached that are physically possible to be formed, yet the examples show that available sizes span over an order of magnitude in diameter). It can be easily predicted that such availability will open completely new avenues in particle design, since the current paradigm is to alter the particle size by changing the polymer structure rather than by changing reaction conditions. The approach shown here is much more efficient and time efficient. Optimizations take merely hours, while a change in polymer structure will take days if not longer for synthesis of polymer alone. Further to the size targeting of particles, we also used our algorithms to obtain Pareto fronts for the interdependent synthesis parameters, namely flow rate, mixing ration and polymer concentration. For the system under investigation, we were able to show exemplary for a 200 nm particle size target that best results are obtained for water contents after mixing of 83-84 %, high flow rates, and high polymer concentrations. Under such conditions, the space time yield is maximized. While determination of such Pareto fronts is time intensive, they are nonetheless obtainable from fully autonomous measurements, and are hence still quite simple to carry out. They allow for detailed analysis of the synthesis system under consideration. Together with the mere screening of the synthesis parameter space, this type of analysis will allow in future a much better general understanding of nanoaggregate formation from block copolymers, and hence pave the way for sophisticated nanoparticle design. Experimental Procedure