Space　situational　awareness　(SSA)　plays　an　important　role　in　maintaining　space　advantages.　Task　planning　is　one　of　the　key　technologies　in　SSA　to　allocate　multiple　tasks　to　multiple　satellites,　so　that　a　satellite　may　be　allocated　to　supervise　multiple　space　objects,　and　a　space　object　may　be　supervised　by　multiple　satellites.　This　paper　proposes　a　hierarchical　and　distributed　task-planning　framework　for　SSA　systems　with　focus　on　fast　and　effective　task　planning　customized　for　SSA.　In　the　framework,　a　global　task-planner　layer　performs　satellite　and　object　clustering,　so　that　satellites　are　clustered　into　multiple　unique　clusters　on　the　basis　of　their　positions,　while　objects　are　clustered　into　multiple　possibly　intersecting　clusters,　hence　allowing　for　a　single　object　to　be　supervised　by　multiple　satellites.　In　each　satellite　cluster,　a　local　task　planner　performs　distributed　task　planning　using　the　contract-net　protocol　(CNP)　on　the　basis　of　the　position　and　velocity　of　satellites　and　objects.　In　addition,　a　customized　discrete　particle　swarm　optimization　(DPSO)　algorithm　was　developed　to　search　for　the　optimal　task-planning　result　in　the　CNP.　Simulation　results　showed　that　the　proposed　framework　can　effectively　achieve　task　planning　among　multiple　satellites　and　space　objects.　The　efficiency　and　scalability　of　the　proposed　framework　are　demonstrated　through　static　and　dynamic　orbital　simulations.