###### Abstract
In a group of vehicles with different models and different health conditions, there is a problem that the objective to minimize the maintenance resources, such as maintenance hours and spare components, conflicts with the objective to maximize the maintenance achievement. In this condition, selective maintenance planning (SMP) comes into being. SMP employs different maintenance strategies for different equipment in the fleet, enabling the fleet to meet the mission requirements while consuming as little maintenance resources as possible. In recent years, selective maintenance planning has attracted more and more attention in the field of equipment maintenance. [PERSON] et al. [17] conducted a study on selective maintenance planning for multistate systems, considering random dependent components and imperfect maintenance, and proposed a multiobjective optimization model considering two objective functions, including the expected value and variance of the system reliability. [PERSON] et al. [18] proposed a sequential planning method for selective maintenance of multistate systems, considering the limited time available for maintenance task between continuous tasks, so as to maximize the probability of the system successfully completing the next mission. [PERSON] et al. [19] summarized a set of criteria considered in selective maintenance optimization into three categories: system characteristics, maintenance characteristics and mission profile characteristics. Based on these criteria, a comprehensive literature review of selective maintenance is made, and it is pointed out that selective maintenance modeling considering different task profiles is a kind of problem worth paying attention to. For the selective maintenance decision problem of a complex equipment system, after the establishment of the mathematical model of the problem, it is necessary to select the appropriate algorithm to optimize the mathematical model, so as to optimize the decision objectives while satisfying the constraint, such as the minimum system failure and maintenance cost, the maximum system reliability or availability, etc. Heuristic algorithms are commonly used in maintenance modeling problems, including Tabu search algorithm [20], genetic algorithm [21], ant colony algorithm (ACO) [18], simulated annealing algorithm [22] (SA), etc. At present, most maintenance decision-solving methods of equipment systems take a single variable as the optimization objective, and after obtaining the maintenance strategy, other indicators are not considered to evaluate the strategy, so that the decision plan ignores some aspects of the requirements, resulting in the reduction in mission capability or the waste of maintenance resources. Therefore, considering multiple indexes in maintenance tasks to make decisions and taking a reasonable way to evaluate the decision scheme is the focus of the current research on maintenance decision-solving methods. [PERSON], [PERSON] et al. [23] established a three-objective selective maintenance model for series-parallel systems and used an improved NSGA-III evolutionary algorithm to solve the problem. NSGA-III reserves some dominated points near reference points instead of only reserving nondominated points in NSGA-II, which contributes to keep the diversity of the population. Therefore, aiming at the mission and reliability driven selective maintenance planning problem of a fleet, this paper establishes a phased solution framework to solve the problem. The main contributions of this paper are as follows: (1) A fleet-level selective maintenance decision model based on virtual age reduction theory is constructed. In the process of model establishment, the optimization objectives of cost and mission capability are both considered. (2) The two-stage problem-solving framework and the corresponding solution method are proposed.