  As organizations increasingly migrate their applications to the cloud, the
optimization of microservices architectures becomes imperative for achieving
sustainability goals. Nonetheless, sustainable deployments may increase costs
and deteriorate performance, thus the identification of optimal tradeoffs among
these conflicting requirements is a key objective not easy to achieve. This
paper introduces a novel approach to support cloud deployment of microservices
architectures by targeting optimal combinations of application performance,
deployment costs, and power consumption. By leveraging genetic algorithms,
specifically NSGA-II, we automate the generation of alternative architectural
deployments. The results demonstrate the potential of our approach through a
comprehensive assessment of the Train Ticket case study.
