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The emergence of smart grid technologies in terms of advanced communication infrastructure, embedded intelligence, diagnostics and monitoring capabilities offers new opportunities for improved transmission asset management strategies (TAMS). Accordingly, power system operators are currently looking for analytics that can make use of transmission asset condition monitors and data already available to make better-informed decisions. This two-part paper introduces a two-stage maintenance scheduler for power transmission assets. Part I begins with the motivation for TAMS and then continues with a two-stage maintenance management model that incorporates joint midterm and short-term maintenance. The first stage involves a midterm asset maintenance scheduler that explicitly considers the asset condition dynamics in terms of failure rate. The second stage introduces a short-term maintenance scheduler with N-1 reliability that schedules the output of the midterm maintenance scheduler in the short run. The midterm and short-term stages are completely decoupled schemes to make the problem computationally tractable. For the sake of exposition here, we focus on the maintenance of grid transformers. The proposed methodology is general, however, and can be extended to other network equipments as well. The characteristics of the proposed model and its benefits are investigated in Part II through several case studies.