Conversion from digital information to spike trains is needed for Spiking Neural Networks. Moreover, it is one of the most important steps for Spiking Neural Networks. This conversion could lead to much information loss depending on which encoding algorithm is used. Another major problem that can occur in a specific use-case is the limited bandwidth for the spikes that get generated through the encoding algorithm. In this thesis, we propose population Step Forward Encoding algorithm. This algorithm takes the signal encoding accuracy of Step Forward encoding algorithm and makes it into a population, generating multiple spike trains. This allows a higher threshold to encode a large part of the signal, increasing the efficiency. We show that population Step Forward Encoding algorithm doesn't just work good for the signal encoding accuracy, but also for the classification accuracy. Moreover, population Step Forward Encoding algorithm does not only have a high efficiency with a low spike count, it can also achieve higher efficiency with higher spike count. Thus, population Step Forward can make most use of a limited bandwidth of spikes.