  Averting the effects of anthropogenic climate change requires a transition
from fossil fuels to low-carbon technology. A way to achieve this is to
decarbonize the electricity grid. However, further efforts must be made in
other fields such as transport and heating for full decarbonization. This would
reduce carbon emissions due to electricity generation, and also help to
decarbonize other sources such as automotive and heating by enabling a
low-carbon alternative. Carbon taxes have been shown to be an efficient way to
aid in this transition. In this paper, we demonstrate how to to find optimal
carbon tax policies through a genetic algorithm approach, using the electricity
market agent-based model ElecSim. To achieve this, we use the NSGA-II genetic
algorithm to minimize average electricity price and relative carbon intensity
of the electricity mix. We demonstrate that it is possible to find a range of
carbon taxes to suit differing objectives. Our results show that we are able to
minimize electricity cost to below \textsterling10/MWh as well as carbon
intensity to zero in every case. In terms of the optimal carbon tax strategy,
we found that an increasing strategy between 2020 and 2035 was preferable. Each
of the Pareto-front optimal tax strategies are at least above
\textsterling81/tCO2 for every year. The mean carbon tax strategy was
\textsterling240/tCO2.
