Tube hydroforming is one of the unconventional metal forming processes in which high fluid pressure and axial feed are used to deform a tube blank in the desired shape. However, production of bi-layered tubular components using this process has not been investigated in detail in spite of the large number of research studies conducted in this area. Bi-layered tubing can be useful in complex working environments as it offers dual properties that a single layer structure doesn’t have. Consequently, for wider implementation of this technology, a detailed investigation on bi-layered tube hydroforming is required. In this research, both single and bi-layered tube hydroforming processes were numerically modelled using the finite element method (ANSYS LS-DYNA). Experiments were conducted to check the numerical models validation. In addition, Response Surface Methodology (RSM) using the Design-Expert statistical software has been employed along with the finite element modelling to attain a detailed investigation of bi-layered tube hydroforming in the X-type and T-type dies. The process outputs were modelled as functions of both the geometrical factors (tube length, tube diameter, die corner radius, and thicknesses of both layers.) and the process parameters (internal pressure coordinates, axial feed, and coefficient of friction.). Furthermore, the desirability approach was used in conjunction with the RSM models to identify the optimal combinations of each the geometrical factors and process parameters that achieve different objectives simultaneously. In addition, a different optimization approach that applies the iterative optimization algorithm in the ANSYS software was implemented in the process optimization. The finite element models of single and bi-layered tube hydroforming processes were experimentally validated. A comparison of both processes was carried out under different loading paths. Also, response surface modelling of the bi-layered tube hydroforming process outputs was successfully achieved, and the main effects and interaction effects of the input parameters on the responses were discussed. Based on the RSM models, the process was optimized by finding the inputs levels at which the desired objectives are satisfied. Finally, a comparison of the RSM based optimization approach and the iterative optimization algorithm was performed based on the optimum results of each technique.