Abstract The paper presents a multi-disciplinary multi-objective design optimization methodology of a combustion chamber effusion cooling system. The optimizer drives an Artificial Neural Network and a Manufacturing Time Model in a repeated analysis scheme in order to increase the combustor liner LCF life and to reduce the liner cooling system manufacturing time, simultaneously. The ANN is trained with a set of fluid/structure/lifing simulations arranged in a three-levels dataset based on a properly designed DOE approach. The CFD simulations are carried out with a reliable and robust in-house developed three-dimensional high resolution reactive viscous flow solver, accounting for conjugate heat transfer approach; the liner structural analysis is performed with a standard FEM code while the liner life assessments are obtained through an in-house developed software operating on the temperature/stress fields. Results demonstrate the validity of the overall approach in a five-dimensional state space with truly moderate computational costs.