Aggregation of sufficiently large electric vehicle (EV) fleets and control over their charging schedules enables aggregators to utilise the flexibility of EV charging in the Day Ahead Market. Optimising the charge scheduling of such fleets enables time-shifting of electricity demand to hours when electricity is cheaper, reducing the electricity cost for charging the entire fleet. Time shifting with scheduled charging is expected to influence the average carbon intensity of the energy used by these vehicles. This work aims to quantify the change in the carbon intensity of energy used by smart charged vehicles. It uses real data collected from over 55, 000 home charging sessions from 1031 chargepoints in the Netherlands in 2018. A simulation was made with a commercial smart charging algorithm to create a scheduled charging profile ex post from the historic EV charging dataset. The simulation resulted in an average price reduction of electricity for the fleet of about 25% relative to unscheduled charging of the same fleet over the same period. The time dependent average carbon intensity of electricity consumed in the Netherlands was used to calculate the mean carbon intensity of the electricity used to charge the fleet over the period in the scheduled and unscheduled charging cases. The results revealed a small decrease in carbon intensity by 1.2%. Analysis reveals that price optimisation can have large effects on the mean carbon intensity of individual sessions in the Dutch grid, but the net effect is averaged out over a large number of sessions and over the year.