Constellation missions are a trending application of the CubeSat standard and, thus far, comprise half of the CubeSats ever released into orbit [6]. They are perceived as a powerful way to explore the space environment due to their ability to cover large areas and capture data from multiple points simultaneously [7]. In contrast to the scientific and commercial capabilities of these affordable small satellites, challenges regarding their reduced dimensions and weight arise. Owing to their slighter size and, consequently, smaller surface area, CubeSats have a lower capacity for harvesting and storing energy, which makes the constantly evolving electrical power system (EPS), a major subsystem, achieve better efficiency within certain weight, size, and cost limits. Such limitations, along with the fact that small satellite missions typically last less time than larger satellite missions, highlight mission planning as a cheap solution capable of improving the overall mission value by optimizing the use of available power resources [8]. This optimal planning can be achieved by scheduling the tasks to be performed through the combination of a representative mathematical model and an optimization algorithm, addressed as the task scheduling problem. Although the single-satellite energy-constrained task scheduling problem has been addressed in the existing literature [9], this work approaches the constellation specifics in a novel way, since one may leverage this structure to improve the mission's QoS by optimally distributing the tasks among satellites during an orbit. By considering, in addition to power limitations, different types of tasks: collective, individual, and with stimulated redundancy; this research proposes an offline energy-aware task scheduling problem formulation regarding the specifics of constellations. In this sense, a multi-objective optimization problem is proposed through an exact mixed-integer linear programming (MILP) solver. Furthermore, we perform an analysis of the Pareto-optimal frontier of the problem, identifying the feasible trade-off points between constellation and individual tasks. This information can be useful to the decision-maker (mission operator) when planning an orbit behavior. By providing such an optimization framework, the idea of estimating an offline task schedule can serve as a basis for the constellation design phase. For example, given a particular orbit, from the simulation of an irradiance model, the engineer can evaluate in an estimated way how the mission value is affected by the inclusion or exclusion of individuals objects. Thus, the engineer can, for example, choose to include or remove a payload, prohibit the execution of tasks simultaneously, enable the redundancy of tasks, and overall better understand the bounds of the constellation. The main contributions of this work are:  
* A novel methodology for optimal task scheduling for constellation of nanosatellites, through a model that extends on that proposed by [9];
* Concept development for aiding engineers in the constellation design phase;
* An analysis of the trade-off between individual and collective satellite tasks. This paper is structured into sections: Section 2 reviews related research regarding the application of optimization algorithms to nanosatellites; Section 3 describes the developed optimization model, detailing its inputs, the multi-objective function to be optimized, and the constraints; Section 4 discusses the results obtained from illustrative scenarios; finally, Section 5 deliberates over the final considerations.