One of the main challenges of today is the energy transition from fossil fuels to green renewable energy. One new concept that can aid this transition is the smart charging of electric vehicles. Smart charging is utilizing the flexible availability of an electric vehicle to charge on a time when this is most valuable, while complying to the constraint that it needs to be fully charged when the vehicle unplugs. This research focuses on the use of smart charging for cost optimization on the ETPA intraday market, in combination with the day ahead market. During this research it has first been investigated what the properties of the intraday market and the available electricity fleet were and which difficulties they pose. One of the most challenging properties were the limited volume constraints placed on the orders in the limit order and the constraint that the sessions had a binary charge speed, meaning they can either fully charge or not charge at all. This combination creates and NP-hard optimization problem. The second main difficulty is online nature of the market, meaning a solution for the hard optimization problem has to be done within seconds. Current research in the field of smart charging on the intraday markets and smart charging in general has not been able to provide this research with a feasible scheduling optimization method. This leads us to the main contribution of this research: the proposal of a new optimization method which computes an (online) approximate scheduling of a fleet, with binary charge settings, that optimizes on the costs of multiple electricity markets including the ETPA intraday and day ahead market. Through empirical testing it has been shown that re optimization in an online environment can be done within maximally a quarter of a second, with a fleet of 10000 EVs per day. Testing has also shown that it can schedule these EVs with a maximum error or 33 kW per PTU, which is an error of 0.16% on a 10000 session per day fleet. The newly proposed optimization methods has immediately been utilized to test the maximum trading possibilities of the ETPA intraday market. Only purchasing on the day ahead and intraday market has shown some possible opportunities, but the real opportunity lies within full trading on the day ahead and intraday market. Cost reductions of up to 6ct/kW could be achieved compared to not smart charging at all. Online tests, using no forecasting model, has shown almost no cost reduction compared to only smart charging on the day ahead market. This is due to the algorithm trading when the least of profit can be made, wasting the profit possibility. This research has provided the first building blocks to achieve a optimization model for smart charging on the ETPA intraday market. For the optimization model to be completely ready it must be able to optimize over PTUs, that have different lengths, and be able to make use of forecasted prices and volumes computed by an external forecasting model.