Magnetic　hyperthermia　is　a　tumor　therapeutic　modality　with　great　potential.　It　ablates　malignant　cells　using　the　heat　from　magnetic　nanoparticles　(MNPs)　subjected　to　an　alternating　magnetic　field.　The　temperature　profile　obtained　during　treatment　is　a　function　of　the　properties　of　the　magnetic　field　and　MNPs,　and　also　the　injection　strategy.　The　injection　strategy　plays　an　important　role　in　the　quality　of　magnetic　hyperthermia　treatment　since　proper　injection　allows　a　more　homogeneous　temperature　field　to　be　obtained　with　less　harm　to　normal　tissue.　This　article　considers　a　Gaussian　distribution　for　a　temperature　around　an　injection　site　(IS)　and　proposes　several　methods　to　optimize　the　treatment　temperature　for　a　proposed　model　by　considering　both　the　injection　dose　and　the　IS　locations　simultaneously.　Two　metaheuristic　algorithms　are　considered　in　this　article,　namely,　particle　swarm　optimization　(PSO)　and　simulated　annealing　(SA).　In　general,　the　PSO　algorithm　presents　better　results　than　the　SA　due　to　its　smaller　dependence　on　the　initial　value,　but　its　convergence　is　slower.　Simulation　results　demonstrate　that　treatment　efficiency　can　be　significantly　increased　by　using　the　proposed　approach　when　the　PSO　algorithm　is　considered　for　the　simultaneous　optimization　of　injection　strategy　and　injection　location　for　magnetic　hyperthermia.