Employees are increasingly using electric vehicles (EVs) as their choice of company car. Charging infrastructure is limited by undersized connection lines and a lack of charging stations on company premises. Upgrades require significant financial investment, time and effort. Smart charging represents an approach to making the most of existing infrastructure while satisfying charging needs. The objective of smart charging depends on the business context. Objectives of interest include fair share maximization, electricity cost minimization, peak demand minimization and load imbalance minimization. During business hours, EV arrivals and departures are predictable while still containing uncertainty. To utilize this knowledge ahead of time, this work presents a novel approach for combining day-ahead and real-time planning for smart charging. First, we model the problem using mixed integer programming for day-ahead planning to precompute schedules. Next, we propose a schedule guided heuristic which takes as input precomputed schedules and adapts them in real-time as new information arrives. Both methods use a parameterized weighting mechanism to flexibly combine and emphasize individual objectives of smart charging. Experimental results from simulations show significant benefits of combining day-ahead and real-time planning over using a single planning approach in isolation. Improvements include increased fair share and decreased energy costs.