The transition to electric heavy-duty fleets requires coordinated planning of charging infrastructure and grid capacity. However, infrastructure decisions are often made under limited information and uncertain grid capacity. This paper introduces an integrated planning framework that links fleet operational characteristics to grid requirements for charging depots, addressing the gap between data-intensive, case-specific optimization studies and early-stage planning needs. The framework integrates a structured survey to derive key fleet characteristics that inform a Mixed Integer Linear Programming (MILP) model, which optimizes charging schedules to minimize peak demand, thereby limiting dependence on grid capacity expansion. The methodology is demonstrated through a case study of a grocery retail company in Denmark undergoing fleet electrification, considering an early phase fleet of 33 battery electric trucks performing regional deliveries with depot return. Results quantify a scenario-based range of feasible grid connection capacities and their implications for achievable electrification levels. Preliminary results indicate significant reductions (up to 70%) in required grid connection capacity compared to a baseline scenario with uncoordinated charging after daily operations. The proposed method offers a low-data entry point for early-stage planning and is particularly relevant in contexts with limited grid availability or long connection lead times. It supports grid planning and charging infrastructure design decisions, including the assessment of flexibility options (e.g., battery storage) to reduce grid connection requirements.