Small satellites have proven to be viable Earth observation platforms. These satellites operate in regimes of increased trajectory uncertainty where traditional planning approaches can lead to sub-optimal task plans, limiting science return. Previous formulations of the space mission planning problem decouple trajectory prediction and planning, which leads to task plans that are less robust to uncertainty. We present a Markov decision process formulation of the problem that accounts for uncertainties by incorporating a distribution of possible collection windows characterized through Monte Carlo simulation. An approximate solution technique yields tasking schedules with rewards comparable to the conventional methods while simultaneously reducing the variations caused by uncertainties and improving runtime.