The presented PhD Thesis was performed in the course of a cooperative project at TU Wien starting in 2014. The publications originated in this project between the Institute of Mechanics and Mechatronics (Division of Control and Process Automation) as research partner, and Siemens AG Österreich as industrial partner. The project has been funded by the Austrian Research Promotion Agency (FFG No. 846041).&#xD;&#xD;The pressing issue of holistic energy simulation and optimization of urban rail vehicles is discussed in this work. The research was focused on the development of new methodologies for modeling the rail vehicle's subsystems, their interaction, the overall vehicle, and design and operation optimization of the vehicle. In this context a new framework for multi-objective mixed-integer optimization of the urban rail vehicle and sensitivity analysis of objectives (e.g. total energy) with respect to design parameters and operational conditions has been developed. Moreover, in a follow-up of the research project the developed methodologies where implemented in an extensive software tool with user-friendly graphical user interface (GUI). &#xD;&#xD;In this PhD Thesis a unique framework for holistic energy simulation and optimization of urban rail vehicles is presented. The overall modular structured model consists of easy to parameterize validated subsystems. All essential energy consuming subsystems can be considered via models of adequate detail level. Interaction between subsystems is simulated and driven by the operational conditions and component-level controllers. Apart from energy signals the framework provides many other time signals, which contain valuable information for design, sizing, testing, and operation of the vehicle. Different work flow variants of the framework enable a case specific simulation or optimization. Furthermore a superordinated multi-objective mixed-integer optimization (MOMIO) can be applied to optimize the system with respect to a multitude of design parameters and operational conditions. Additionally a Brute-Force-Method (BFM) simulating all possible combinations enables to analyze the sensitivity of objectives (e.g. total energy) with respect to those parameters.