Mass and efficiency are key performance indicators for the development and design of future electric power systems (EPS) for more-electric aircraft (MEA). However, to enable consideration of high-level EPS architecture design trades, there is a requirement for modelling and simulation based analysis to support this activity. The predominant focus to date has been towards the more detailed aspects of analysis, however there is also a significant requirement to be able to perform rapid high-level trades of candidate architectures and technologies.Such a capability facilitates a better appreciation of the conflicting desires to maximize availability and efficiency in candidate MEA architectures, whilst minimizing the overall system mass. It also provides a highly valuable and quantitative assessment of the systemic impact of new enabling technologies being considered for MEA applications. Without this capability, predesign assessments are often time consuming and of a qualitative manner.Accordingly, this paper will present a steady state pre-design analysis tool for MEA architectures, which enables analysis of the architecture performance at different stages of the flight profile. By providing drag and drop models of key MEA electrical power system components configured for common voltage and power levels, the tool facilitates the rapid construction of candidate architectures which then enables the subsequent quantitative assessment of overall system mass and efficiency. Key to the credibility and usefulness of this tool, is the appropriate marrying of validated fundamental mathematical models (for example in the evaluation of system losses), up-to-date data driven models (for example, relating to component masses or power densities) and the flexibility to incorporate new models of technologies under consideration. The paper will describe these core elements and present selected case studies demonstrating potential uses of the tool in architecture assessment and down-selection, technology impact, and design-point sensitivity analysis.